视频序列中的运动目标检测与跟踪研究
|
摘要
视频序列中的运动目标检测与跟踪研究利用计算机视觉和视频分析方法,对视频输入装置得到的图像序列进行自动分析,实现对运动目标的检测、定位和跟踪,为更高层的视频理解和场景解释提供底层对象和分析依据。它是计算机科学、图像工程、模式识别、人工智能等多学科的结晶。随着理论算法和硬件处理技术的快速发展,视频目标检测跟踪技术已经应用在武器导航、科学探测等工业领域以及小区监控、智能交通、自动驾驶、人机交互等民用领域,并将有着更为广泛的应用前景。
目前,视频目标检测跟踪的研究在理论和应用上还面临着很多困难,如在检测中如何兼顾目标的完整性和边界的精确性,在跟踪中如何适应目标尺度变化、模板变化及速度、轨迹变化等。国内外很多学者对此进行了大量研究和探索,本文即是在这些研究的基础上,针对上述问题展开研究工作。主要工作可概括如下:
在运动目标检测方面,针对检测的准确性和完整性要求,提出了一种基于马尔科夫随机场模型的时-空联合目标检测实现方法。在空域检测中,提出了Mean-Shift约束标记的分水岭分割方法。该方法利用了Mean-Shift算法的聚类特性,在序列帧的特征域和图像域寻找概率密度集中区,并结合两个域的信息标记出图像的视觉关注区,作为分水岭分割的约束条件,避免了分水岭算法容易产生的过分割现象。建立了简化S-TMRF模型,构建相应的后验能量函数,将空域检测和时域检测的信息统一在MRF-MAP检测框架下,得到了更为准确、完整,符合人的视觉感受的检测目标。
在运动目标跟踪方面,针对传统Mean-Shift采用固定核窗宽,难以适应目标尺度变化,易造成定位不准甚至失踪的缺点。提出了基于边界力的核窗宽自适应调节方法。在对Mean-Shift算法中目标模型进行分析的基础上,引入了边界力的概念,在Mean-Shift跟踪中加入跟踪目标边界附近空间点的特征匹配约束,从而能够获得当前跟踪目标的位置和尺度,并根据检测估计到的尺度来调节核函数带宽。该算法与传统的三步法相比,减少了运算量和运算复杂度,可以更稳定的跟踪空间尺度变化较大的目标。
研究了Mean-Shift跟踪中的状态判断问题,在分析目标和背景特征相对关系的基础上,引入特征增强函数,构造了新的背景模板。在跟踪过程中,通过对候选目标与两个模板相似度系数的综合分析,可以准确判断跟踪所处的状态及可能的产生原因,并采取相应的模板调整策略。由于综合考虑了目标与背景的相互影响,算法对跟踪状态的辨别更为准确。
研究了粒子滤波在轨迹复杂的快速运动目标跟踪中的应用。针对粒子滤波算法需要大量粒子来近似描述目标状态,耗时较多的问题,利用Mean-Shift算法在重采样前将粒子收敛到靠近目标真实状态的区域内,使粒子的分布更为合理有效。算法减少了粒子滤波跟踪所需要的粒子数,提高了算法的跟踪效率。
最后,在算法研究的基础上,针对室内监控这一特定背景环境,设计搭建了一个基于TI公司高性能DSP处理器TMS320DM642芯片的目标监控系统测试平台。根据硬件系统本身的性能和特点,在算法结构、代码结构和DM642芯片功能三个层次上进行了优化,以满足实时性的需要。在系统设计中,采用主-从端结构分流运算负荷,并充分利用DSP的运算性能和可编程性,使系统具有可重构性,能够适应多种图像处理的要求,具有一定的通用性
Moving object detection and tracking employs the methods of computer vision and video processing to automatically analyze the image sequence from video input device. Its purpose is to detect, locate and track moving object and provide video understanding and scene comprehension with object and analysis basis. It spans many subjects including computer science, image engineering, pattern recognition and artificial intelligence, etc. With the rapid development of algorithm theory and hardware technique, object detection and tracking technology has already been applied in industrial fields,such as navigation weapons, scientific exploration, and civil fields, such as community monitoring, traffic flow, driver assistance, and human-computer interaction, etc. It will be used more widely.
As to video object detection and tracking, there are still many problems no matter in theory research or in applications. Such as how to balance between the integrality of object and the accuracy of boundary during object detection, and how to adapt scale, template, speed and trajectory changes during object tracking. Large numbers of researchers have devoted themselves in the area. Based on the current research, our study is carried out for these issues. The main work can be summarized as follow:
In the aspect of moving object detection, for the demand of accuracy and integrality, a spatio-temporal joint detection method based on markov random field (MRF) was proposed. In spatial detection, a watershed segmentation algorithm with Mean-Shift mark constraint was presented. It used the clustering property of Mean-Shift to find the probability density center in both image and feather space. Then the visual area of concern was marked based on the information from both space and was regarded as the constraint condition of watershed segmentation. This method avoided over-segmentation. Based on the simplified S-TMRF model, the posterior energy function was defined. After that, the information from both temporal space and spatial space were integrated in MRF-MAP framework to detect moving object. The result is more robust and precise.
In the aspect of moving object tracking, to improve deficiency that the kernel bandwidth of Mean-Shift is not changeable which makes it difficult to tracking object with changeable scale, a novel adaptive scale updating algorithm based on boundary force was presented. Based on the analysis of the target model, the boundary force was introduced, which added feature matching constraint of pixels near object boundary to Mean-Shift tracking. The adaptive algorithm could locate the target’s position and could adjust the bandwidth of kernel-function according to the estimated scale. Compared with traditional three-step method, our algorithm reduced the computation and computing complexity and could track the object with large-scale changes more stably.
How to determine the tracking status was studied here. Based on the analysis of the relationship between object features and background features, the feature enhancement function was introduced and the novel background template was constructed. During tracking process, with the comprehensive analysis of similarity coefficients of candidate object and the templates, the proposed algorithm could accurately judge the tracking status and the cause of interference, then take corresponding template updating strategy. This method could make the judgment of tracking status more precise because of the analysis of the interaction between object and background.
The particle filter algorithm was studied to track fast moving object with complex trajectory. Aiming at the problem that particle filter requires many particles to approximately describe state of object, which is more time-consuming, the Mean-Shift algorithm was used to converge the particles to area of real state before re-sample. It made distribution of particles more reasonable. Because the particle description became more rational, the number of particle required was reduced and the tracking efficiency was improved.
According to the indoor environment, an object surveillant test platform was designed and constructed based on our algorithm. The test platform used TI’s high-performance DSP TMS320DM642 as the core chip. Optimization has been carried out on three levels such as algorithm structure, code structure and chip function according to the property and feature of hardware system. In the system design, the host-guest structure was used to share the computational load. This platform made full use of the computational power and programmability of DSP。It was reconfigurable and was suitable for many kind of image processing.
目前,视频目标检测跟踪的研究在理论和应用上还面临着很多困难,如在检测中如何兼顾目标的完整性和边界的精确性,在跟踪中如何适应目标尺度变化、模板变化及速度、轨迹变化等。国内外很多学者对此进行了大量研究和探索,本文即是在这些研究的基础上,针对上述问题展开研究工作。主要工作可概括如下:
在运动目标检测方面,针对检测的准确性和完整性要求,提出了一种基于马尔科夫随机场模型的时-空联合目标检测实现方法。在空域检测中,提出了Mean-Shift约束标记的分水岭分割方法。该方法利用了Mean-Shift算法的聚类特性,在序列帧的特征域和图像域寻找概率密度集中区,并结合两个域的信息标记出图像的视觉关注区,作为分水岭分割的约束条件,避免了分水岭算法容易产生的过分割现象。建立了简化S-TMRF模型,构建相应的后验能量函数,将空域检测和时域检测的信息统一在MRF-MAP检测框架下,得到了更为准确、完整,符合人的视觉感受的检测目标。
在运动目标跟踪方面,针对传统Mean-Shift采用固定核窗宽,难以适应目标尺度变化,易造成定位不准甚至失踪的缺点。提出了基于边界力的核窗宽自适应调节方法。在对Mean-Shift算法中目标模型进行分析的基础上,引入了边界力的概念,在Mean-Shift跟踪中加入跟踪目标边界附近空间点的特征匹配约束,从而能够获得当前跟踪目标的位置和尺度,并根据检测估计到的尺度来调节核函数带宽。该算法与传统的三步法相比,减少了运算量和运算复杂度,可以更稳定的跟踪空间尺度变化较大的目标。
研究了Mean-Shift跟踪中的状态判断问题,在分析目标和背景特征相对关系的基础上,引入特征增强函数,构造了新的背景模板。在跟踪过程中,通过对候选目标与两个模板相似度系数的综合分析,可以准确判断跟踪所处的状态及可能的产生原因,并采取相应的模板调整策略。由于综合考虑了目标与背景的相互影响,算法对跟踪状态的辨别更为准确。
研究了粒子滤波在轨迹复杂的快速运动目标跟踪中的应用。针对粒子滤波算法需要大量粒子来近似描述目标状态,耗时较多的问题,利用Mean-Shift算法在重采样前将粒子收敛到靠近目标真实状态的区域内,使粒子的分布更为合理有效。算法减少了粒子滤波跟踪所需要的粒子数,提高了算法的跟踪效率。
最后,在算法研究的基础上,针对室内监控这一特定背景环境,设计搭建了一个基于TI公司高性能DSP处理器TMS320DM642芯片的目标监控系统测试平台。根据硬件系统本身的性能和特点,在算法结构、代码结构和DM642芯片功能三个层次上进行了优化,以满足实时性的需要。在系统设计中,采用主-从端结构分流运算负荷,并充分利用DSP的运算性能和可编程性,使系统具有可重构性,能够适应多种图像处理的要求,具有一定的通用性
Moving object detection and tracking employs the methods of computer vision and video processing to automatically analyze the image sequence from video input device. Its purpose is to detect, locate and track moving object and provide video understanding and scene comprehension with object and analysis basis. It spans many subjects including computer science, image engineering, pattern recognition and artificial intelligence, etc. With the rapid development of algorithm theory and hardware technique, object detection and tracking technology has already been applied in industrial fields,such as navigation weapons, scientific exploration, and civil fields, such as community monitoring, traffic flow, driver assistance, and human-computer interaction, etc. It will be used more widely.
As to video object detection and tracking, there are still many problems no matter in theory research or in applications. Such as how to balance between the integrality of object and the accuracy of boundary during object detection, and how to adapt scale, template, speed and trajectory changes during object tracking. Large numbers of researchers have devoted themselves in the area. Based on the current research, our study is carried out for these issues. The main work can be summarized as follow:
In the aspect of moving object detection, for the demand of accuracy and integrality, a spatio-temporal joint detection method based on markov random field (MRF) was proposed. In spatial detection, a watershed segmentation algorithm with Mean-Shift mark constraint was presented. It used the clustering property of Mean-Shift to find the probability density center in both image and feather space. Then the visual area of concern was marked based on the information from both space and was regarded as the constraint condition of watershed segmentation. This method avoided over-segmentation. Based on the simplified S-TMRF model, the posterior energy function was defined. After that, the information from both temporal space and spatial space were integrated in MRF-MAP framework to detect moving object. The result is more robust and precise.
In the aspect of moving object tracking, to improve deficiency that the kernel bandwidth of Mean-Shift is not changeable which makes it difficult to tracking object with changeable scale, a novel adaptive scale updating algorithm based on boundary force was presented. Based on the analysis of the target model, the boundary force was introduced, which added feature matching constraint of pixels near object boundary to Mean-Shift tracking. The adaptive algorithm could locate the target’s position and could adjust the bandwidth of kernel-function according to the estimated scale. Compared with traditional three-step method, our algorithm reduced the computation and computing complexity and could track the object with large-scale changes more stably.
How to determine the tracking status was studied here. Based on the analysis of the relationship between object features and background features, the feature enhancement function was introduced and the novel background template was constructed. During tracking process, with the comprehensive analysis of similarity coefficients of candidate object and the templates, the proposed algorithm could accurately judge the tracking status and the cause of interference, then take corresponding template updating strategy. This method could make the judgment of tracking status more precise because of the analysis of the interaction between object and background.
The particle filter algorithm was studied to track fast moving object with complex trajectory. Aiming at the problem that particle filter requires many particles to approximately describe state of object, which is more time-consuming, the Mean-Shift algorithm was used to converge the particles to area of real state before re-sample. It made distribution of particles more reasonable. Because the particle description became more rational, the number of particle required was reduced and the tracking efficiency was improved.
According to the indoor environment, an object surveillant test platform was designed and constructed based on our algorithm. The test platform used TI’s high-performance DSP TMS320DM642 as the core chip. Optimization has been carried out on three levels such as algorithm structure, code structure and chip function according to the property and feature of hardware system. In the system design, the host-guest structure was used to share the computational load. This platform made full use of the computational power and programmability of DSP。It was reconfigurable and was suitable for many kind of image processing.
引文
[1] Haritaoglu I., Harwood D., Davis L. W4: real-time surveillance of people and their activities[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2000, 22(8):809-830.
[2] Collins R., Lipton A., et al. A system for video surveillance and monitoring: vsam final report. CMU-RI-TR-0012, Robotic institute Carnegie mellon university, 2000.
[3] Collins R., Lipton A. and Kanade T. Introduction to the special section on video surveillance[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2000, 22(8):745-746.
[4] Magee D. Tracking multiple vehicles using foreground, background and motion models[J]. Image and Vision Computing, 2004, 22(2):143-155.
[5] Koller D., Daniilidis K., Nagel H. Model-based object tracking in monocular image sequences of road traffic scene[J]. International Journal of Computer Vision, 1993, 10(3):257-281.
[6] Dubuisson-Jolly M., Lakshmanan S., Jain A.K. Vehicle segmentation and classification using deformable templates[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1996, 18(3):293-308.
[7] Coifman B., Beymer D., Mclauchlan P., Malik J. A real-time computer vision system for vehicle tracking and traffic surveillance[J]. Transportation Research Part C, 1998, 6(4):271-288.
[8] Tai J., Tsang S., Lin C., Song K. Real-time image tracking for automatic traffic monitoring and enforment application[J], Image and Vision Computing, 2004, 22(6):485-501.
[9] Zhu Z., Xu G., Yang B., Shi D., Lin X. VISATRAM: A real-time vision system for automatic traffic monitoring[J]. Image and Vision Computing, 2000, 18(10):781-794.
[10] Haag M., Nagel H. Tracking of complex driving maneuvers in traffic image sequences[J] Image and Vision Computing, 1998, 16(8):517-527.
[11]侯志强,韩崇昭.视频跟踪技术综述[J].自动化学报, 2006, 32(4):603-617.
[12] Pai C., Tyan H., Liang Y., Liao H.M., Chen S. Pedestrian detection and tracking at crossroads[J]. Pattern Recognition, 2004, 37(5):1025-1034.
[13] Masoud O., Papanikolopoulos N.P. A novel method for tracking and counting pedestrians in real-time using a single camera[J]. IEEE Trans. on Vehicular Technology, 2001, 50(5):1267-1278.
[14] Avidan S. Support vector tracking[J]. Proc. of IEEE Conf. Computer Vision and Pattern Recognition, 2001, Vol.1,184-191.
[15] Handmann U., Kalinke T., Tzomakas C., Werner M., and Von Seelen W. Computer vision for driver assistance systems[J]. Proc. SPIE, 1998, Vol.3364:136-147.
[16] Foresti G.L., Murino V., Regazzoni C. Vehicle recognition and tracking from road image sequences[J]. IEEE Trans. on Vehicular Technology, 1999, 48(1):301-318.
[17] Betke M., Haritaoglu E., Davis L.S. Real-time multiple vehicle detection and tracking from a moving vehicle[J]. Machine Vision and Applications, 2000, 12(2):69-83.
[18] Ferryman J.M., Maybnak S.J., Worrall A.D. Visual surveillance for moving vehicle[J]. International Journal of Computer Vision, 2000, 37(2):187-197.
[19] Turk M. Visual interaction with life-like characters[C]. In: Proc. of IEEE Int’l. Conf. on Automatic Face and Gesture Recognition, Killington, Vermont, USA, 1996, 368-373.
[20] Al-Jarrah O. and Halawani A. Recognition of gestures in Arabic sign language using neuro-fuzzy systems[J]. Artificial Intelligence, 2001, 133(2):117-138.
[21] Al-Jarrah O. and Al-Omari F.A. Improving gesture recognition in the Arabic Sign Language using texture analysis[J]. Applied Artificial Intelligence, 2007, 21(1):11-33.
[22] Ong E.J. and Bowden R. A boosted classifier tree for hand shape detection[C]. In: Proc. of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, 2004, 889-894.
[23] Munib Q., Habeeb M., Takruri B. and Al-Malik H.A. American sign language (ASL) recognition based on Hough transform and neural networks[J]. Expert Systems with Applications, 2007, 32(1):24- 37.
[24] Bui T.D. and Nguyen L.T. Recognizing postures in vietnamese sign language with MEMS accelerometers[J]. IEEE Sensors Journal, 2007, 7(5):707-712.
[25] Oz C. and Leu M.C. Linguistic properties based on American sign language isolated word recognition with artificial neural networks using a sensory glove and motion tracker[J]. Neurocomputing, 2007, 70(16-18):2891-2901.
[26] Nickel K. and Stiefelhagen R. Visual recognition of pointing gestures for human-robot interaction[J]. Image and Vision Computing, 2007, 25(12):1875-1884.
[27] Hu H.H., Jia P., Lu T. and Yuan K. Head gesture recognition for hands-free control of an intelligent wheelchair[J]. Industrial Robot, 2007, 34(1):60-68.
[28] Lakany H., Haycs G., Hazlewood M. and Hillman S. Human walking: tracking and analysis[C]. In: Proc. of IEEE Colloquium on Motion Analysis and Tracking, Savoy Place, London, 1999, 5:1-14.
[29] Mitra S. and Acharya T. Gesture recognition: A survey[J]. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 2007, 37(3):311-324.
[30] Erol A., Bebis G., Nicolescu M., Boyle R.D. and Twombly X. Vision-based hand pose estimation: A review[J]. Computer Vision and Image Understanding, 2007, 108(1-2):52-73.
[31]王亮,胡卫明,谭铁牛.人运动的视觉分析综述[J].计算机学报, 2002, 25(3):225-237.
[32] Kumar R., Swhney H., Hsu S., et al. Aerial video surveillance and exploitation[J]. Proceedings of IEEE, 2001, 89(10):1382-1402.
[33] Tao H., Sawhney H.S., Kumar R. Object tracking with Bayesian estimation of dynamic layer representations[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002, 24(1):75-89.
[34] Tsao T., Wen Z. Image-based target tracking through rapid sensor orientation change[J]. SPIE Optical Engineering, 2002, 41(3):697-703.
[35] Rui Y., Huang T.S., Chang S.F. Image retrieval: Current techniques, promising directions and open issues[J]. Journal of Visual Communication and Image Representation, 1999,10(4):39-62.
[36] Rui Y., Huang T.S., Ortega M., Mehrotra S. Relevance feedback: A power tool for interactive content-based image retrieval[J]. IEEE Trans. on Circuits and Systems for Video Technology, 1998,8(5):644-655.
[37] Shao-yi C., Yu-Wen H., Bing-Yu H. Fast video segmentation lgorithm with shadow cancellation, global motion compensation and adaptive threshod techniques[J]. IEEE Trans. on Multimedia, 2004, 6(5):732-748.
[38] Shao-yi C., Shyh-Yih M., Liang-Gee C. Efficient moving object segmentation algorithm using background registration technique[J]. IEEE Trans. on Circuits and Systems for Video Technology, 2002, 12(7):577-586.
[39] Shao-Yi C., Shyh-Yih M. Liang-Gee C. A hybrid morhology processing units architecture for real-time video segmentation systems[C]. IEEE International Symposium on Circuits and Systems, Vol.5:275-278.
[40] Thomas M., King N.N. Automatic segmentation of moving objects for video object plane generation[J]. IEEE Trans. on Circuits and Systems for Video Technology, 1998, 8(5):525-538.
[41] Thomas M. King N.N. Video segmentation for content-based coding[J]. IEEE Trans. on Circuits Systems for Video Technolgy, 1999, 9(8):1190-1203.
[42]韩军,熊璋,侯亚荣等.交互式分割视频运动对象的研究与实现[J].中国图像图形学报, 2003, 8(2):169-175.
[43] Deniel Gatica-Perez, Ghuang Gu, Ming-Ting Sun. Semantic video object extraction using four-band watershed and partition lattice operators[J]. IEEE Trans. on Circuits and Systems for Video Technology, 2001, 11(5):603-618.
[44] Sun S.J., Haynor David R., Kim Y.M. Semiautomatic video object segmentation using vsnakes[J]. IEEE Trans. on Circuits and Systems for Video Technology, 13(1):75-82.
[45] Gu C., Lee M.C., Semi-automatic segmentation and tracking of semantic video object[J]. IEEE Trans. on Circuits and Systems for Video Technology, 1998, 8(5):572-584.
[46] Correia P., Pereira F. The role of analysis in contect-based video coding and indexing[J], Signal Processing, 1998, 66:125-142.
[47] Kim C., Hwang J. Fast and automatic video object segmentation and tracking for content-based applications[J]. IEEE Trans. on Circuits and Systems for Video Technology, 2002, 12(2):122-129.
[48] Tsai Y., Lai C. Hung Y., et al. A bayesian approach to video object segmentation via merging 3-d watershed volumes[J]. IEEE Trans. on Circuits and Systems for Video Technology, 2005, 15(1)175-180.
[49] Mezaris V., Kompatsiaris I. Strintzis m. G. Video object segmentation using bayes-based temporal tracking and trajectory-based region merging[J]. IEEE Trans. on Circuits and Systems for Video Technology, 2004, 14(6):782-795.
[50]陈韩锋,戚飞虎.视频对象分割中基于GIBBS随机场模型的时空分割结合方法[J].电子学报, 2004, 32(1):34-37.
[51] Li L.Y., Huang W.M., Gu Y.H., et al. Statistical modeling of complex backgrounds for foreground object detection[J]. IEEE Trans. on Image Processing, 2004, 13(11):1459-1472.
[52] Gupte S., Masoud O., Martin R.F.K., et al. Detection and classification vehicles[J], IEEE Trans. on Intellignet Transportation Systems, 2002, 3(1):37-47.
[53] Papadimitriou T., Diamantaras K.I., Strintzis M.G. Video scene segmentation using spatial contours and 3-d robust motion estimation[J]. IEEE Trans. on Circuits and Systems for Video Technology, 2004, 14(4):485-497.
[54] Tsaig Y., Averbuch A. Automatic segmentation of moving objects in video sequences: a region labeling approach[J]. IEEE Trans. on Circuits and Systems for Video Technology, 2002, 12(7):597-612.
[55] Paragios N. Ramesh V. A MRF-based approach for real-time subway monitoring[C]. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, 2001, VOL.1:1034-1040.
[56] Lee S., Ouyang C. Du A. A neuro-fuzzy approach for segmentation of human objects in image sequences[J]. IEEE Trans. on Systems, Man, and Cybernetics, 2003, 33(3):420-437.
[57] Thomas Meier. Segmentation for video object plane extraction and reduction of coding artifact[Ph.D dissertation]. Dept. of Electrical and Electronic Engineering. The University of Western Australia, 1998.
[58] Torr P.H.S. Motion segmentation and outlier detection[Ph.D dissertation]. Oxford University, 1995.
[59] Tekalp A. M. Digital Video Processing[M].北京:清华大学出版社, 1998.
[60] Verri A., Uras S., DeMicheli E. Motion segmentation from optical flow[C]. In: Proc. of the 5TH alvey Vision Conf. Brighton, UK, 1989, 209-214.
[61] Barron J., Fleet D., Beauchemin S. Performance of optical flow techniques[J]. International Journal of Computer Vision, 1994, 12(1):42-77.
[62] Mae Y., Shirai Y., Miura J., Kuno Y. Object tracking in cluttered background based on optical flow and edges[C]. In: Proc the 13TH intern. Conf. on Pattern Recognition, 1996, 196-200.
[63] Mae Y., et al. Optical flow based realtime object tracking by active vision system[C]. In: Proc. of 2ND Japan-France Congress on Mechatronics, 1994, 545-548.
[64] Neri A., Colonnese S., Russo G., Talone P. Automatic moving object and background separation[J]. Signal Processing, 66(2):219-232.
[65] Kim M., Choi J.G., Kim D., et al. A VOP generation tool: automatic segmentation of moving objects inimage sequences based on spatio-temporal information[J]. IEEE Trans. on Circuits and Systems for Video Technology, 1999, 9(8):1216-1226.
[66] Alatan A.A., Onural L., Wollborn M., Mech R., et al. Image sequence analysis for emerging interactive multimedia services-the European cost 211 framework[J]. IEEE Trans. on Circuits and Systems for Video Technology, 1998, 8(7):802-813.
[67] McKenna S., Jabri S., Duric Z., Rosenfeld A., Wechsler H. Tracking groups of people[J]. Computer Vision and Image Understanding, 2000, 80(1):42-56.
[68] Kilger M. A shadow handler in a video-based real-time traffic monitoring system[C]. In: Proc. of IEEE Workshop on Applications of Computer Vision, Palm Springs, CA, 1992, 1060-1066.
[69] Karmann K., Brandt A. Moving object recognition using an adaptive background memory[M]. In: Cappellini V, Time-varying Image Processing and Moving Object Recognition, 2, Elsevier, Amsterdam, Netherlands, 1990, 289-296.
[70] Brink A.B. Gray level thresholding of images using a correlation criterion[J]. Pattern Recognition Letters, 1989, 9(5):335-341.
[71] Mardia K.V. and Hainsworth T.J. A spatial thresholding method for image segmentation[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1988, 10(6):919-927.
[72] Pun T. A new method for gray level picture thresholding using the entropy of the histogram[J]. Signal Processing, 1980, 2(3):223-237.
[73] Halt L.O., Bensaid A.M., Clarke L.P., Velthuizen R.P., et al. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonances images of the brain[J]. IEEE Trans. on Neural Networks, 1992, 3(5):672-681.
[74] Liu Y.W. and Lee S.U. On the color image segmentation algorithm based on the thresholding and the fussy c-means techniques[J]. Pattern Recognition, 1990, 23(9):935-952.
[75] Sang H.P., Dong Y. and Sang UK L. Color image segmentation based on 3-d clustering: morphological approach[J]. Pattern Recognition, 1998, 31(8):1061-1076.
[76] Schmid P. Segmentation of digitized dermstoscopic images by two dimensional color clustering[J]. IEEE Trans. on Medical Imaging, 1999, 31(8):1061-1076.
[77]胡世英,周源华.模糊选择多分辨率Kohonen聚类网络用于灰度图像分割[J].电子学报, 1999, 27(10):34-37.
[78] Wang J.P. Stochastic relaxation on partitions with connected components and its application to image segmentation[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1998, 20(6):619-636.
[79] Peleg S. A new probabilistic relaxation scheme[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1980, 2(4):362-369.
[80] Themeau A., Borel N. A region growing and merging algorithm to color segmentation[J]. Pattern Recognition, 1997, 30(7):1191-1203.
[81] Sandor T., Metcalf D. and Kim Y.J. Segmentation of brain CT image using the concept of region growing[J]. International Journal of Bio-Medical Computing, 1991, 29(2):133-147.
[82] Adams R. and Bischof L. Seeded region growing[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1994, 16(6):641-647.
[83] Bhergholm F. Edge focusing[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1987, 9(9):726-741.
[84] Chanda B., Kundu M.K., Vani Padmaja Y. A multi-scale morphologic edge detector[J]. Pattern Recognition, 1998, 31(10):1469-1478.
[85] Ziou D. and Tabbone S. A multi-scale edge detector[J]. Pattern Recognitioin, 1993, 26(9):1305-1314.
[86] Brown M.A., Blackwell K.T. Khalak H.G., et al. Multi-scale edge detection and feature binding: an intergated approach[J]. Pattern Recognition, 1983, 31(10):1479-1490.
[87] Kass M., Witkin A., Terzopoulous D. Snakes: Active contour models[J]. International Journal of Computer Vision, 1987, 1(4):321-331.
[88] Mech R. and Wollborn M. A noice robust method for 2d shape estimation of moving objects in video sequence considering a moving camera[J]. Signal Processing, 1998, 66(2):203-217.
[89] Wang Y., Koe K., Tan T., et al. Spatiotemporal video segmentation based on graphical models[J]. IEEE Trans. on Image Processing, 2005, 14(7):536-549.
[90] Liu Y., Zheng Y.F. Video object segmentation and tracking usingф-learning classification[J]. IEEE Trans. on Circuits and System for Video Technology, 1996, 6(4):419-423.
[91] Chien S., Huang Y., Chen L. Predictive watershed: A fast watershed algorith for video segmentation[J]. IEEE Trans. on Circuits and System for Video Technology, 2003, 13(5):453-461.
[92] Smith E.A. and Phillips D.R. Automated cloud tracking using precisely aligned digital ATS pictures[J]. IEEE Trans. Computers C, 1972, 21(7):715-729.
[93] Bregler C., Malik J. Tracking people with twists and exponential maps[C]. In: Proc. of IEEE Comp. Soc. Conf. on Computer vision and pattern Recognition(CVPR), 1998, 8-15.
[94] Kakadiaris L., Metaxas D. Model-based estimation of 3D human motion[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2000, 22(12):1453-1459.
[95] Pahlavan k. active robot vision and primary ocular processes[Ph.D DISSERTATION]. Dept. of Numerical Analysis and Computing Science, KTH, Stockholm, Sweden, 1993.
[96] Thompson W.B., Lechleider P., Stuck E.R. Detecting moving objects using the rigidity constraint[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1993, 15(2):162-165.
[97] Meyer F.G., Bouthemy P. Region-based tracking using affine motion models in long image sequences[C]. In: CVGIP: Image Understanding, 1994, 60(2):119-140.
[98] Fan J., Yu J., Fujita G. Spatio-temporal segmentation for compact video representation[J]. Signal Process: Image Communication, 2001, 16:553-566.
[99] Eckehard S., Peter E., Bernd G. Motion-based analysis and segmentation of image sequence using 3-D scene models[J]. Signal Processing, 1998, 66(2):233-247.
[100] Chuang G., Chieh L.M. Semiautomatic segmentation and tracking of semantic video objects[J]. IEEE Trans. on Circuits and Systems for Video Technology, 1998, 8(5):572-584.
[101] Aggarawal J.K. Nandhakumar N. On the computation of motion from sequences of images-A review[J]. Proceedings of IEEE, 1988, 76(8):917-935.
[102] Smith S.M. Brady J.M. SUSAN-A new approach to low level image processing[J]. International Journal of Computer Vision, 1997, 23(1):45-78.
[103] Odobez J.M. Bouthemy P. Direct incremental model-based image motion segmentation for video analysis[J]. Signal Processing, 1998, 66(2):143-155.
[104] Patras L., Hendriks E.A., Lagendijk R.L. Video segmentation by MAP labeling of watershed segments[J] IEEE Trans. on Pattern Analysis and Machine Intelligence, 2001,23(3):326-332.
[105] Markandey V., Reid A., Wang S. Motion estimation for moving target detection[J]. IEEE Trans. on Aerospace and Electronic Systems, 1996, 32(3):866-874.
[106] Anderson M. Tracking methods in computer vision[Ph.D dissertation]. In Dept. of Numerical Analysis and Computing Science, KTH, Stockholm, Sweden, 1994.
[107] Zhang Z. Token tracking in a cluttered scene[J]. Image and Vision Computing, 1994, 12(2):110-120.
[108] Jung S.K., Wohn K.Y. A model-based 3-D tracking of rigid objects from a sequence of multiple perspective views[J]. Pattern Recognition Letters. 1998, 19(5-6):499-512;
[109] Nickels K., Hutchinson S. Estimating uncertainty in SSD-based feature tracking[J]. Image and Vision Computing. 2002, 20(1):47-58.
[110] Uhlin T., Nordlund P., Maki A.,Eklundh J.O. Towards an active visual observer[C]. In: Proc. of IEEE 5th Int’l. Conf. on Computer Vision, Cambridge, MA, 1995, 679-680
[111] Haritaoglu I., Harwood D., Davis L.S. Ghost: A human body part labeling system using silhouettes [C]. In: Proc. of the 14th Int’l. Conf. on pattern Pattern Recognition, Brisbane, Qld., Australia, 1998, Vol.1:77-82.
[112] Matsumoto Y., Sakai K., Inaba M., Inoue H. View-based approach to robot navigation[C]. In: Proc. of IEEE/RSJ Int’l. Conf. on Intelligent Robots and Systems, Takamatsu, Japan, 2000, Vol.3:1702-1708.
[113] Ito K., Sakane S. Robust view-based visual tracking with detection of occlusions[C]. In: Proc. of IEEE Int’l. Conf. on Robotics and Automation, 2001, Vol.2:1207-1213.
[114] Leymarie F., Levine M.D. Tracking deformable objects in the plane using an active contour model[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1993, 15(6):617-624.
[115] Cohen L.D. Note on active contour models and ballons[J]. Computer vision, Graphics and Image Processing: Image Understanding. 1991, 53(2):211-218.
[116] Lu S., Metaxas D., Samaras D., Oliensis J. Using multiple cues for hand tracking and model refinement[C]. In: Proc. of IEEE Conf. on computer vision and pattern recognition, 2003, Vol.2:443-450.
[117] Dubes, R.C., Jain, A.K. Random field modes in image analysis[J]. J. Appl. Stat., 1989, 16(2):131-164.
[118] Geiger, D., Yuilli, A. A common framework for image segmentation[J]. Int’l. J. Comput. Vis., 1991, 6(3):227-243.
[119] Geman S., Geman, D., Graffigne, C., and Dong, P. Boundary detection by constrained optimization[J], IEEE Trans. on Pattern Analysis And Machine Intelligence, 1990, 12:609-628.
[120] Marroquin, J., Mitter, S., and Poggio, T. Probabilistic solution of ill-posed problems in computation vision[J], J. Amer. Stat. Assoc, 1987, 82(397):76-89.
[121] Bouthemy. P., and Lalande P., Detection and tracking objects based on statistical regularization method in space and time[C]. In: Proc. of 1st europ. Conf. Vis., Antibes, France, 1990, 301-311.
[122] Murray D.W., and Buxton B.F. Scene segmentation from visual motion using global optimization[J], IEEE Trans. on Pattern Analysis and Machine Intelligence, 1987, 9(2):384-401.
[123] Black,M.J., and Anandan, P., Robust dynamic motion estimation over time[C]. In: proc. Conf. ComPut. Vis. Patt. ReCog. ,Hawaii .June.1991, 296-302.
[124] Heitz, F., and Bouthemy, P., Multimodal motion estimation and segmentation using Markov random Field[C]. In: Proc. of 10th Conf. Patt. Recog., Atlantie City. .June.1990, 378-383.
[125] Konarad, J., and Dubois, E. Bayesian estimation of discontinuous motion in image using simulated annealing[C], Proe. Vis. InterfaCe, London Ontario. Canada. June.1989:296-302.
[126] Li S.Z. Markov random field modeling in image analysis[M]. New York: Springer-Verlag,2001.
[127]郑肇葆.图像分析的马尔柯夫随机场方法[M]湖北:武汉测绘科技大学出版社, 2000.
[128] Geman S, Geman D. Stochastic relaxation, gibbs distribution and Bayesian restoration of images[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1984,6:721-741.
[129] Besag J. On the statistical analysis of dirty pictures[J]. Journal of Royal Statistic Society, Series B, 1986, 68:259-302.
[130]陈木法.随机场概论[J].数学进展, 1989, 18(3):294-322.
[131] Mao J. and Jain A.K. Texture classification and segmentation using multiresolution simultaneous autoregressive models[J]. Patter Recognition, 1992, 25(2):173-188.
[132] Metropolis N., Rosenbluth A., et al. Equation of state calculations by fast computing machines[J]. J. Chem. Physics, 1953, 21:1087-1092.
[133] Jeng F.C., Woods J.M. Compound gauss-markov random fields for image estimation[J]. IEEE Trans. on Signal Processing., 1991, 39:638-697.
[134] Blake A., Zisserman A., Visual reconstruction[M]. Cambridge: MIT Press, 1987.
[135] Geiger D., Girosi F., Parallel and deterministic algorithms for MRFs: surface reconstruction and integrations[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1991, 13(5):401-412.
[136] Cerny V., Thermodynamical aproach to the traveling salesman problem: An efficent algorithm.[J]. Journal of Optimization Theory and Applications, 1985, 45(1):41-51.
[137]贾静平.图像序列中目标跟踪技术研究[博士学位论文].西安:西北工业大学, 2006.
[138] Doucet A., Freitas J. De, Gordon N. Sequential monte carlo methods in practice[M]. New York: springer-verlag, 2001.
[139] Doucet A., Godsill S., Andrieu C. On sequential monte carlo sampling methods for bayesian filtering[J]. Statist. Computer, 2000, 10(3):197-208.
[140] Arulampalam S. and Ristic B. Comparson of the particle filter with range parameterised and modified polar EKF’s for angle-only tracking[J]. Signal and Data Processing of Small Target, SPIE, 2000:288-299.
[141] Strom J., Jebara T., Basu S., Pentland A. Real time tracking and modeling of faces: An EKF-based analysis by synthesis approach[C]. IEEE International Workshop on Modelling People, 1999, 55-59.
[142] Gordon N., Salmon D. and Smith A. A novel approach to nonlinear/nongaussian Bayesian state estimation[J]. IEEE Proceedings on Radar and Signal Processing, 1993, 140(2):107-113.
[143] Isard M., Blake A. Condensation-conditional density propagation for visual tracking[J]. International Journal of Computer Vision, 1998, 29(1):5-28.
[144] Ristic B., Arulampalam S., Gordon N. Beyond the kalman filter, particle filter for tracking application[M]. Boston, MA, Artech House, 2004.
[145] Vincent L. and Soille P. Watersheds in digital spaces:an efficient algorithm based on immersion simulations[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1991, 13(6):583-598.
[146] Sharma G., Trussell HJ. Digital color image[J], IEEE Trans. on Image Processing. 1997, 6(7):901-932.
[147] Aach T., Kaup A. and Mester R. Statistical model-based change detection in moving video[J]. Signal Processing, 1993,31(2):165-180.
[148]詹劲峰,戚飞虎,王海龙.基于时空马尔可夫随机场的运动目标分割技术[J],通信学报, 2000, 21(11):45-49.
[149] Comaniciu D., Ramesh V., Meer P. Real-time tracking of non-rigid objects using mean shift[C]. IEEE Conf. Computer Vision and Pattern Recognition, 2000, vol.2:142-149.
[150] Fukunaga K, Hostetler LD. The estimation of the gradient of a density function, withapplications in pattern recognition[J]. IEEE Trans. on Information Theory, 1975, 21(1):32-40.
[151] Cheng YZ. Mean shift, mode seeking and clustering[J]. IEEE Trans. on .Pattern Analysis and Machine Intelligence, 1995, 17(8):790-799.
[152] Comaniciu D., Meer P. Mean shift analysis and applications[C]. In: Proc. of the 7th IEEE Int’l Conf. on Computer Vision(ICCV), 1999. 1197-1203.
[153] Comaniciu D., Meer P. Mean shift: A robust application toward feature space analysis[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002, 24(5):603-619.
[154] Comaniciu D., Meer P. Robust analysis of feature spaces, color image segmentation[C]. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition(CVPR’97). 1997. 750-755.
[155] Nummiaro K., Koller-Meier E., Van Gool L. Color features for tracking non-rigid objects[J]. Special Issue on Visual Surveillance, Chinese Journal of Automation, 2003, 29(3):345-355.
[156]程建,杨杰.一种基于均值移位的红外目标跟踪新方法[J].红外与毫米波学报, 2005, 24(3):231-235.
[157] Comaniciu D., Ramesh V., Meer P. Kernel-Based object tracking[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2003,25(5):564-575.
[158] Bradski GR. Real time face and object tracking as a component of a perceptual user interface[C]. In: Proc. of the 4th IEEE Workshop on Applications of Computer Vision(WACV). 1998. 214-219.
[159] Collins RT. Mean-shift blob tracking through scale space[C]. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR '03). 2003. 234-240.
[160]彭宁嵩,杨杰,刘志,张风超. Mean-shift跟踪算法中核函数窗宽的自动选取[J].软件学报, 2005, 16(9): 1542-1550.
[161]朱胜利,朱善安.基于卡尔曼滤波器组的Mean Shift模板更新算法[J].中国图象图形学报, 2007,12(3):460-465.
[162]孙中森,孙俊喜,宋建中等.一种抗遮挡的运动目标跟踪算法[J].光学精密工程, 2007, 15(2):267-271.
[163] Test Image Sequences for Face Tracking by Stan Birchfield[OL], available: http://www.ces.clemson.edu/~stb/research/headtracker /seq, 2008, 7.
[164] Iscaps C. Pets2006 Dataset S7 Camera2[OL], available: http://ftp.cs.rdg.ac.uk/PETS2006/S3-T70-A.zip, 2008, 7.
[165] Pitt M. K., Shephard N. Filtering via simulation: auxiliary particle filter[J]. Journal of the American Statistical Association, 1999, 94(446): 590-599.
[166] Musso C., Oudjane N., Le Gland F. Improving regularized particle filters[M]. In:Sequential Monte Carlo Methods in Practics, Statistics for Engineering and Information Science, New York: Springer-Verlag, 2001.
[167] Kotecha J. H., Djuric P.M. Gaussian Particle Filtering[J]. IEEE Trans. on Signal Processing, 2003, 51(10): 2592-2601.
[168] Texas Instrument Co. TMS320DM642:Video/imaging fixed-point digital signal processor. [OL],available: http://www.ti.com/lit/gpn/tms320dm642, 2007.1.
[169]方帅.计算机智能视频监控系统关键技术研究[博士学位论文].沈阳:东北大学, 2005.
[170] Chang C., Ansari R. Kernel particle filter for visual tracking[J]. IEEE Signal Processing Letters, 2005, 12(3): 242-245.
[2] Collins R., Lipton A., et al. A system for video surveillance and monitoring: vsam final report. CMU-RI-TR-0012, Robotic institute Carnegie mellon university, 2000.
[3] Collins R., Lipton A. and Kanade T. Introduction to the special section on video surveillance[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2000, 22(8):745-746.
[4] Magee D. Tracking multiple vehicles using foreground, background and motion models[J]. Image and Vision Computing, 2004, 22(2):143-155.
[5] Koller D., Daniilidis K., Nagel H. Model-based object tracking in monocular image sequences of road traffic scene[J]. International Journal of Computer Vision, 1993, 10(3):257-281.
[6] Dubuisson-Jolly M., Lakshmanan S., Jain A.K. Vehicle segmentation and classification using deformable templates[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1996, 18(3):293-308.
[7] Coifman B., Beymer D., Mclauchlan P., Malik J. A real-time computer vision system for vehicle tracking and traffic surveillance[J]. Transportation Research Part C, 1998, 6(4):271-288.
[8] Tai J., Tsang S., Lin C., Song K. Real-time image tracking for automatic traffic monitoring and enforment application[J], Image and Vision Computing, 2004, 22(6):485-501.
[9] Zhu Z., Xu G., Yang B., Shi D., Lin X. VISATRAM: A real-time vision system for automatic traffic monitoring[J]. Image and Vision Computing, 2000, 18(10):781-794.
[10] Haag M., Nagel H. Tracking of complex driving maneuvers in traffic image sequences[J] Image and Vision Computing, 1998, 16(8):517-527.
[11]侯志强,韩崇昭.视频跟踪技术综述[J].自动化学报, 2006, 32(4):603-617.
[12] Pai C., Tyan H., Liang Y., Liao H.M., Chen S. Pedestrian detection and tracking at crossroads[J]. Pattern Recognition, 2004, 37(5):1025-1034.
[13] Masoud O., Papanikolopoulos N.P. A novel method for tracking and counting pedestrians in real-time using a single camera[J]. IEEE Trans. on Vehicular Technology, 2001, 50(5):1267-1278.
[14] Avidan S. Support vector tracking[J]. Proc. of IEEE Conf. Computer Vision and Pattern Recognition, 2001, Vol.1,184-191.
[15] Handmann U., Kalinke T., Tzomakas C., Werner M., and Von Seelen W. Computer vision for driver assistance systems[J]. Proc. SPIE, 1998, Vol.3364:136-147.
[16] Foresti G.L., Murino V., Regazzoni C. Vehicle recognition and tracking from road image sequences[J]. IEEE Trans. on Vehicular Technology, 1999, 48(1):301-318.
[17] Betke M., Haritaoglu E., Davis L.S. Real-time multiple vehicle detection and tracking from a moving vehicle[J]. Machine Vision and Applications, 2000, 12(2):69-83.
[18] Ferryman J.M., Maybnak S.J., Worrall A.D. Visual surveillance for moving vehicle[J]. International Journal of Computer Vision, 2000, 37(2):187-197.
[19] Turk M. Visual interaction with life-like characters[C]. In: Proc. of IEEE Int’l. Conf. on Automatic Face and Gesture Recognition, Killington, Vermont, USA, 1996, 368-373.
[20] Al-Jarrah O. and Halawani A. Recognition of gestures in Arabic sign language using neuro-fuzzy systems[J]. Artificial Intelligence, 2001, 133(2):117-138.
[21] Al-Jarrah O. and Al-Omari F.A. Improving gesture recognition in the Arabic Sign Language using texture analysis[J]. Applied Artificial Intelligence, 2007, 21(1):11-33.
[22] Ong E.J. and Bowden R. A boosted classifier tree for hand shape detection[C]. In: Proc. of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, 2004, 889-894.
[23] Munib Q., Habeeb M., Takruri B. and Al-Malik H.A. American sign language (ASL) recognition based on Hough transform and neural networks[J]. Expert Systems with Applications, 2007, 32(1):24- 37.
[24] Bui T.D. and Nguyen L.T. Recognizing postures in vietnamese sign language with MEMS accelerometers[J]. IEEE Sensors Journal, 2007, 7(5):707-712.
[25] Oz C. and Leu M.C. Linguistic properties based on American sign language isolated word recognition with artificial neural networks using a sensory glove and motion tracker[J]. Neurocomputing, 2007, 70(16-18):2891-2901.
[26] Nickel K. and Stiefelhagen R. Visual recognition of pointing gestures for human-robot interaction[J]. Image and Vision Computing, 2007, 25(12):1875-1884.
[27] Hu H.H., Jia P., Lu T. and Yuan K. Head gesture recognition for hands-free control of an intelligent wheelchair[J]. Industrial Robot, 2007, 34(1):60-68.
[28] Lakany H., Haycs G., Hazlewood M. and Hillman S. Human walking: tracking and analysis[C]. In: Proc. of IEEE Colloquium on Motion Analysis and Tracking, Savoy Place, London, 1999, 5:1-14.
[29] Mitra S. and Acharya T. Gesture recognition: A survey[J]. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 2007, 37(3):311-324.
[30] Erol A., Bebis G., Nicolescu M., Boyle R.D. and Twombly X. Vision-based hand pose estimation: A review[J]. Computer Vision and Image Understanding, 2007, 108(1-2):52-73.
[31]王亮,胡卫明,谭铁牛.人运动的视觉分析综述[J].计算机学报, 2002, 25(3):225-237.
[32] Kumar R., Swhney H., Hsu S., et al. Aerial video surveillance and exploitation[J]. Proceedings of IEEE, 2001, 89(10):1382-1402.
[33] Tao H., Sawhney H.S., Kumar R. Object tracking with Bayesian estimation of dynamic layer representations[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002, 24(1):75-89.
[34] Tsao T., Wen Z. Image-based target tracking through rapid sensor orientation change[J]. SPIE Optical Engineering, 2002, 41(3):697-703.
[35] Rui Y., Huang T.S., Chang S.F. Image retrieval: Current techniques, promising directions and open issues[J]. Journal of Visual Communication and Image Representation, 1999,10(4):39-62.
[36] Rui Y., Huang T.S., Ortega M., Mehrotra S. Relevance feedback: A power tool for interactive content-based image retrieval[J]. IEEE Trans. on Circuits and Systems for Video Technology, 1998,8(5):644-655.
[37] Shao-yi C., Yu-Wen H., Bing-Yu H. Fast video segmentation lgorithm with shadow cancellation, global motion compensation and adaptive threshod techniques[J]. IEEE Trans. on Multimedia, 2004, 6(5):732-748.
[38] Shao-yi C., Shyh-Yih M., Liang-Gee C. Efficient moving object segmentation algorithm using background registration technique[J]. IEEE Trans. on Circuits and Systems for Video Technology, 2002, 12(7):577-586.
[39] Shao-Yi C., Shyh-Yih M. Liang-Gee C. A hybrid morhology processing units architecture for real-time video segmentation systems[C]. IEEE International Symposium on Circuits and Systems, Vol.5:275-278.
[40] Thomas M., King N.N. Automatic segmentation of moving objects for video object plane generation[J]. IEEE Trans. on Circuits and Systems for Video Technology, 1998, 8(5):525-538.
[41] Thomas M. King N.N. Video segmentation for content-based coding[J]. IEEE Trans. on Circuits Systems for Video Technolgy, 1999, 9(8):1190-1203.
[42]韩军,熊璋,侯亚荣等.交互式分割视频运动对象的研究与实现[J].中国图像图形学报, 2003, 8(2):169-175.
[43] Deniel Gatica-Perez, Ghuang Gu, Ming-Ting Sun. Semantic video object extraction using four-band watershed and partition lattice operators[J]. IEEE Trans. on Circuits and Systems for Video Technology, 2001, 11(5):603-618.
[44] Sun S.J., Haynor David R., Kim Y.M. Semiautomatic video object segmentation using vsnakes[J]. IEEE Trans. on Circuits and Systems for Video Technology, 13(1):75-82.
[45] Gu C., Lee M.C., Semi-automatic segmentation and tracking of semantic video object[J]. IEEE Trans. on Circuits and Systems for Video Technology, 1998, 8(5):572-584.
[46] Correia P., Pereira F. The role of analysis in contect-based video coding and indexing[J], Signal Processing, 1998, 66:125-142.
[47] Kim C., Hwang J. Fast and automatic video object segmentation and tracking for content-based applications[J]. IEEE Trans. on Circuits and Systems for Video Technology, 2002, 12(2):122-129.
[48] Tsai Y., Lai C. Hung Y., et al. A bayesian approach to video object segmentation via merging 3-d watershed volumes[J]. IEEE Trans. on Circuits and Systems for Video Technology, 2005, 15(1)175-180.
[49] Mezaris V., Kompatsiaris I. Strintzis m. G. Video object segmentation using bayes-based temporal tracking and trajectory-based region merging[J]. IEEE Trans. on Circuits and Systems for Video Technology, 2004, 14(6):782-795.
[50]陈韩锋,戚飞虎.视频对象分割中基于GIBBS随机场模型的时空分割结合方法[J].电子学报, 2004, 32(1):34-37.
[51] Li L.Y., Huang W.M., Gu Y.H., et al. Statistical modeling of complex backgrounds for foreground object detection[J]. IEEE Trans. on Image Processing, 2004, 13(11):1459-1472.
[52] Gupte S., Masoud O., Martin R.F.K., et al. Detection and classification vehicles[J], IEEE Trans. on Intellignet Transportation Systems, 2002, 3(1):37-47.
[53] Papadimitriou T., Diamantaras K.I., Strintzis M.G. Video scene segmentation using spatial contours and 3-d robust motion estimation[J]. IEEE Trans. on Circuits and Systems for Video Technology, 2004, 14(4):485-497.
[54] Tsaig Y., Averbuch A. Automatic segmentation of moving objects in video sequences: a region labeling approach[J]. IEEE Trans. on Circuits and Systems for Video Technology, 2002, 12(7):597-612.
[55] Paragios N. Ramesh V. A MRF-based approach for real-time subway monitoring[C]. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, 2001, VOL.1:1034-1040.
[56] Lee S., Ouyang C. Du A. A neuro-fuzzy approach for segmentation of human objects in image sequences[J]. IEEE Trans. on Systems, Man, and Cybernetics, 2003, 33(3):420-437.
[57] Thomas Meier. Segmentation for video object plane extraction and reduction of coding artifact[Ph.D dissertation]. Dept. of Electrical and Electronic Engineering. The University of Western Australia, 1998.
[58] Torr P.H.S. Motion segmentation and outlier detection[Ph.D dissertation]. Oxford University, 1995.
[59] Tekalp A. M. Digital Video Processing[M].北京:清华大学出版社, 1998.
[60] Verri A., Uras S., DeMicheli E. Motion segmentation from optical flow[C]. In: Proc. of the 5TH alvey Vision Conf. Brighton, UK, 1989, 209-214.
[61] Barron J., Fleet D., Beauchemin S. Performance of optical flow techniques[J]. International Journal of Computer Vision, 1994, 12(1):42-77.
[62] Mae Y., Shirai Y., Miura J., Kuno Y. Object tracking in cluttered background based on optical flow and edges[C]. In: Proc the 13TH intern. Conf. on Pattern Recognition, 1996, 196-200.
[63] Mae Y., et al. Optical flow based realtime object tracking by active vision system[C]. In: Proc. of 2ND Japan-France Congress on Mechatronics, 1994, 545-548.
[64] Neri A., Colonnese S., Russo G., Talone P. Automatic moving object and background separation[J]. Signal Processing, 66(2):219-232.
[65] Kim M., Choi J.G., Kim D., et al. A VOP generation tool: automatic segmentation of moving objects inimage sequences based on spatio-temporal information[J]. IEEE Trans. on Circuits and Systems for Video Technology, 1999, 9(8):1216-1226.
[66] Alatan A.A., Onural L., Wollborn M., Mech R., et al. Image sequence analysis for emerging interactive multimedia services-the European cost 211 framework[J]. IEEE Trans. on Circuits and Systems for Video Technology, 1998, 8(7):802-813.
[67] McKenna S., Jabri S., Duric Z., Rosenfeld A., Wechsler H. Tracking groups of people[J]. Computer Vision and Image Understanding, 2000, 80(1):42-56.
[68] Kilger M. A shadow handler in a video-based real-time traffic monitoring system[C]. In: Proc. of IEEE Workshop on Applications of Computer Vision, Palm Springs, CA, 1992, 1060-1066.
[69] Karmann K., Brandt A. Moving object recognition using an adaptive background memory[M]. In: Cappellini V, Time-varying Image Processing and Moving Object Recognition, 2, Elsevier, Amsterdam, Netherlands, 1990, 289-296.
[70] Brink A.B. Gray level thresholding of images using a correlation criterion[J]. Pattern Recognition Letters, 1989, 9(5):335-341.
[71] Mardia K.V. and Hainsworth T.J. A spatial thresholding method for image segmentation[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1988, 10(6):919-927.
[72] Pun T. A new method for gray level picture thresholding using the entropy of the histogram[J]. Signal Processing, 1980, 2(3):223-237.
[73] Halt L.O., Bensaid A.M., Clarke L.P., Velthuizen R.P., et al. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonances images of the brain[J]. IEEE Trans. on Neural Networks, 1992, 3(5):672-681.
[74] Liu Y.W. and Lee S.U. On the color image segmentation algorithm based on the thresholding and the fussy c-means techniques[J]. Pattern Recognition, 1990, 23(9):935-952.
[75] Sang H.P., Dong Y. and Sang UK L. Color image segmentation based on 3-d clustering: morphological approach[J]. Pattern Recognition, 1998, 31(8):1061-1076.
[76] Schmid P. Segmentation of digitized dermstoscopic images by two dimensional color clustering[J]. IEEE Trans. on Medical Imaging, 1999, 31(8):1061-1076.
[77]胡世英,周源华.模糊选择多分辨率Kohonen聚类网络用于灰度图像分割[J].电子学报, 1999, 27(10):34-37.
[78] Wang J.P. Stochastic relaxation on partitions with connected components and its application to image segmentation[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1998, 20(6):619-636.
[79] Peleg S. A new probabilistic relaxation scheme[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1980, 2(4):362-369.
[80] Themeau A., Borel N. A region growing and merging algorithm to color segmentation[J]. Pattern Recognition, 1997, 30(7):1191-1203.
[81] Sandor T., Metcalf D. and Kim Y.J. Segmentation of brain CT image using the concept of region growing[J]. International Journal of Bio-Medical Computing, 1991, 29(2):133-147.
[82] Adams R. and Bischof L. Seeded region growing[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1994, 16(6):641-647.
[83] Bhergholm F. Edge focusing[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1987, 9(9):726-741.
[84] Chanda B., Kundu M.K., Vani Padmaja Y. A multi-scale morphologic edge detector[J]. Pattern Recognition, 1998, 31(10):1469-1478.
[85] Ziou D. and Tabbone S. A multi-scale edge detector[J]. Pattern Recognitioin, 1993, 26(9):1305-1314.
[86] Brown M.A., Blackwell K.T. Khalak H.G., et al. Multi-scale edge detection and feature binding: an intergated approach[J]. Pattern Recognition, 1983, 31(10):1479-1490.
[87] Kass M., Witkin A., Terzopoulous D. Snakes: Active contour models[J]. International Journal of Computer Vision, 1987, 1(4):321-331.
[88] Mech R. and Wollborn M. A noice robust method for 2d shape estimation of moving objects in video sequence considering a moving camera[J]. Signal Processing, 1998, 66(2):203-217.
[89] Wang Y., Koe K., Tan T., et al. Spatiotemporal video segmentation based on graphical models[J]. IEEE Trans. on Image Processing, 2005, 14(7):536-549.
[90] Liu Y., Zheng Y.F. Video object segmentation and tracking usingф-learning classification[J]. IEEE Trans. on Circuits and System for Video Technology, 1996, 6(4):419-423.
[91] Chien S., Huang Y., Chen L. Predictive watershed: A fast watershed algorith for video segmentation[J]. IEEE Trans. on Circuits and System for Video Technology, 2003, 13(5):453-461.
[92] Smith E.A. and Phillips D.R. Automated cloud tracking using precisely aligned digital ATS pictures[J]. IEEE Trans. Computers C, 1972, 21(7):715-729.
[93] Bregler C., Malik J. Tracking people with twists and exponential maps[C]. In: Proc. of IEEE Comp. Soc. Conf. on Computer vision and pattern Recognition(CVPR), 1998, 8-15.
[94] Kakadiaris L., Metaxas D. Model-based estimation of 3D human motion[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2000, 22(12):1453-1459.
[95] Pahlavan k. active robot vision and primary ocular processes[Ph.D DISSERTATION]. Dept. of Numerical Analysis and Computing Science, KTH, Stockholm, Sweden, 1993.
[96] Thompson W.B., Lechleider P., Stuck E.R. Detecting moving objects using the rigidity constraint[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1993, 15(2):162-165.
[97] Meyer F.G., Bouthemy P. Region-based tracking using affine motion models in long image sequences[C]. In: CVGIP: Image Understanding, 1994, 60(2):119-140.
[98] Fan J., Yu J., Fujita G. Spatio-temporal segmentation for compact video representation[J]. Signal Process: Image Communication, 2001, 16:553-566.
[99] Eckehard S., Peter E., Bernd G. Motion-based analysis and segmentation of image sequence using 3-D scene models[J]. Signal Processing, 1998, 66(2):233-247.
[100] Chuang G., Chieh L.M. Semiautomatic segmentation and tracking of semantic video objects[J]. IEEE Trans. on Circuits and Systems for Video Technology, 1998, 8(5):572-584.
[101] Aggarawal J.K. Nandhakumar N. On the computation of motion from sequences of images-A review[J]. Proceedings of IEEE, 1988, 76(8):917-935.
[102] Smith S.M. Brady J.M. SUSAN-A new approach to low level image processing[J]. International Journal of Computer Vision, 1997, 23(1):45-78.
[103] Odobez J.M. Bouthemy P. Direct incremental model-based image motion segmentation for video analysis[J]. Signal Processing, 1998, 66(2):143-155.
[104] Patras L., Hendriks E.A., Lagendijk R.L. Video segmentation by MAP labeling of watershed segments[J] IEEE Trans. on Pattern Analysis and Machine Intelligence, 2001,23(3):326-332.
[105] Markandey V., Reid A., Wang S. Motion estimation for moving target detection[J]. IEEE Trans. on Aerospace and Electronic Systems, 1996, 32(3):866-874.
[106] Anderson M. Tracking methods in computer vision[Ph.D dissertation]. In Dept. of Numerical Analysis and Computing Science, KTH, Stockholm, Sweden, 1994.
[107] Zhang Z. Token tracking in a cluttered scene[J]. Image and Vision Computing, 1994, 12(2):110-120.
[108] Jung S.K., Wohn K.Y. A model-based 3-D tracking of rigid objects from a sequence of multiple perspective views[J]. Pattern Recognition Letters. 1998, 19(5-6):499-512;
[109] Nickels K., Hutchinson S. Estimating uncertainty in SSD-based feature tracking[J]. Image and Vision Computing. 2002, 20(1):47-58.
[110] Uhlin T., Nordlund P., Maki A.,Eklundh J.O. Towards an active visual observer[C]. In: Proc. of IEEE 5th Int’l. Conf. on Computer Vision, Cambridge, MA, 1995, 679-680
[111] Haritaoglu I., Harwood D., Davis L.S. Ghost: A human body part labeling system using silhouettes [C]. In: Proc. of the 14th Int’l. Conf. on pattern Pattern Recognition, Brisbane, Qld., Australia, 1998, Vol.1:77-82.
[112] Matsumoto Y., Sakai K., Inaba M., Inoue H. View-based approach to robot navigation[C]. In: Proc. of IEEE/RSJ Int’l. Conf. on Intelligent Robots and Systems, Takamatsu, Japan, 2000, Vol.3:1702-1708.
[113] Ito K., Sakane S. Robust view-based visual tracking with detection of occlusions[C]. In: Proc. of IEEE Int’l. Conf. on Robotics and Automation, 2001, Vol.2:1207-1213.
[114] Leymarie F., Levine M.D. Tracking deformable objects in the plane using an active contour model[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1993, 15(6):617-624.
[115] Cohen L.D. Note on active contour models and ballons[J]. Computer vision, Graphics and Image Processing: Image Understanding. 1991, 53(2):211-218.
[116] Lu S., Metaxas D., Samaras D., Oliensis J. Using multiple cues for hand tracking and model refinement[C]. In: Proc. of IEEE Conf. on computer vision and pattern recognition, 2003, Vol.2:443-450.
[117] Dubes, R.C., Jain, A.K. Random field modes in image analysis[J]. J. Appl. Stat., 1989, 16(2):131-164.
[118] Geiger, D., Yuilli, A. A common framework for image segmentation[J]. Int’l. J. Comput. Vis., 1991, 6(3):227-243.
[119] Geman S., Geman, D., Graffigne, C., and Dong, P. Boundary detection by constrained optimization[J], IEEE Trans. on Pattern Analysis And Machine Intelligence, 1990, 12:609-628.
[120] Marroquin, J., Mitter, S., and Poggio, T. Probabilistic solution of ill-posed problems in computation vision[J], J. Amer. Stat. Assoc, 1987, 82(397):76-89.
[121] Bouthemy. P., and Lalande P., Detection and tracking objects based on statistical regularization method in space and time[C]. In: Proc. of 1st europ. Conf. Vis., Antibes, France, 1990, 301-311.
[122] Murray D.W., and Buxton B.F. Scene segmentation from visual motion using global optimization[J], IEEE Trans. on Pattern Analysis and Machine Intelligence, 1987, 9(2):384-401.
[123] Black,M.J., and Anandan, P., Robust dynamic motion estimation over time[C]. In: proc. Conf. ComPut. Vis. Patt. ReCog. ,Hawaii .June.1991, 296-302.
[124] Heitz, F., and Bouthemy, P., Multimodal motion estimation and segmentation using Markov random Field[C]. In: Proc. of 10th Conf. Patt. Recog., Atlantie City. .June.1990, 378-383.
[125] Konarad, J., and Dubois, E. Bayesian estimation of discontinuous motion in image using simulated annealing[C], Proe. Vis. InterfaCe, London Ontario. Canada. June.1989:296-302.
[126] Li S.Z. Markov random field modeling in image analysis[M]. New York: Springer-Verlag,2001.
[127]郑肇葆.图像分析的马尔柯夫随机场方法[M]湖北:武汉测绘科技大学出版社, 2000.
[128] Geman S, Geman D. Stochastic relaxation, gibbs distribution and Bayesian restoration of images[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1984,6:721-741.
[129] Besag J. On the statistical analysis of dirty pictures[J]. Journal of Royal Statistic Society, Series B, 1986, 68:259-302.
[130]陈木法.随机场概论[J].数学进展, 1989, 18(3):294-322.
[131] Mao J. and Jain A.K. Texture classification and segmentation using multiresolution simultaneous autoregressive models[J]. Patter Recognition, 1992, 25(2):173-188.
[132] Metropolis N., Rosenbluth A., et al. Equation of state calculations by fast computing machines[J]. J. Chem. Physics, 1953, 21:1087-1092.
[133] Jeng F.C., Woods J.M. Compound gauss-markov random fields for image estimation[J]. IEEE Trans. on Signal Processing., 1991, 39:638-697.
[134] Blake A., Zisserman A., Visual reconstruction[M]. Cambridge: MIT Press, 1987.
[135] Geiger D., Girosi F., Parallel and deterministic algorithms for MRFs: surface reconstruction and integrations[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1991, 13(5):401-412.
[136] Cerny V., Thermodynamical aproach to the traveling salesman problem: An efficent algorithm.[J]. Journal of Optimization Theory and Applications, 1985, 45(1):41-51.
[137]贾静平.图像序列中目标跟踪技术研究[博士学位论文].西安:西北工业大学, 2006.
[138] Doucet A., Freitas J. De, Gordon N. Sequential monte carlo methods in practice[M]. New York: springer-verlag, 2001.
[139] Doucet A., Godsill S., Andrieu C. On sequential monte carlo sampling methods for bayesian filtering[J]. Statist. Computer, 2000, 10(3):197-208.
[140] Arulampalam S. and Ristic B. Comparson of the particle filter with range parameterised and modified polar EKF’s for angle-only tracking[J]. Signal and Data Processing of Small Target, SPIE, 2000:288-299.
[141] Strom J., Jebara T., Basu S., Pentland A. Real time tracking and modeling of faces: An EKF-based analysis by synthesis approach[C]. IEEE International Workshop on Modelling People, 1999, 55-59.
[142] Gordon N., Salmon D. and Smith A. A novel approach to nonlinear/nongaussian Bayesian state estimation[J]. IEEE Proceedings on Radar and Signal Processing, 1993, 140(2):107-113.
[143] Isard M., Blake A. Condensation-conditional density propagation for visual tracking[J]. International Journal of Computer Vision, 1998, 29(1):5-28.
[144] Ristic B., Arulampalam S., Gordon N. Beyond the kalman filter, particle filter for tracking application[M]. Boston, MA, Artech House, 2004.
[145] Vincent L. and Soille P. Watersheds in digital spaces:an efficient algorithm based on immersion simulations[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1991, 13(6):583-598.
[146] Sharma G., Trussell HJ. Digital color image[J], IEEE Trans. on Image Processing. 1997, 6(7):901-932.
[147] Aach T., Kaup A. and Mester R. Statistical model-based change detection in moving video[J]. Signal Processing, 1993,31(2):165-180.
[148]詹劲峰,戚飞虎,王海龙.基于时空马尔可夫随机场的运动目标分割技术[J],通信学报, 2000, 21(11):45-49.
[149] Comaniciu D., Ramesh V., Meer P. Real-time tracking of non-rigid objects using mean shift[C]. IEEE Conf. Computer Vision and Pattern Recognition, 2000, vol.2:142-149.
[150] Fukunaga K, Hostetler LD. The estimation of the gradient of a density function, withapplications in pattern recognition[J]. IEEE Trans. on Information Theory, 1975, 21(1):32-40.
[151] Cheng YZ. Mean shift, mode seeking and clustering[J]. IEEE Trans. on .Pattern Analysis and Machine Intelligence, 1995, 17(8):790-799.
[152] Comaniciu D., Meer P. Mean shift analysis and applications[C]. In: Proc. of the 7th IEEE Int’l Conf. on Computer Vision(ICCV), 1999. 1197-1203.
[153] Comaniciu D., Meer P. Mean shift: A robust application toward feature space analysis[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002, 24(5):603-619.
[154] Comaniciu D., Meer P. Robust analysis of feature spaces, color image segmentation[C]. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition(CVPR’97). 1997. 750-755.
[155] Nummiaro K., Koller-Meier E., Van Gool L. Color features for tracking non-rigid objects[J]. Special Issue on Visual Surveillance, Chinese Journal of Automation, 2003, 29(3):345-355.
[156]程建,杨杰.一种基于均值移位的红外目标跟踪新方法[J].红外与毫米波学报, 2005, 24(3):231-235.
[157] Comaniciu D., Ramesh V., Meer P. Kernel-Based object tracking[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2003,25(5):564-575.
[158] Bradski GR. Real time face and object tracking as a component of a perceptual user interface[C]. In: Proc. of the 4th IEEE Workshop on Applications of Computer Vision(WACV). 1998. 214-219.
[159] Collins RT. Mean-shift blob tracking through scale space[C]. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR '03). 2003. 234-240.
[160]彭宁嵩,杨杰,刘志,张风超. Mean-shift跟踪算法中核函数窗宽的自动选取[J].软件学报, 2005, 16(9): 1542-1550.
[161]朱胜利,朱善安.基于卡尔曼滤波器组的Mean Shift模板更新算法[J].中国图象图形学报, 2007,12(3):460-465.
[162]孙中森,孙俊喜,宋建中等.一种抗遮挡的运动目标跟踪算法[J].光学精密工程, 2007, 15(2):267-271.
[163] Test Image Sequences for Face Tracking by Stan Birchfield[OL], available: http://www.ces.clemson.edu/~stb/research/headtracker /seq, 2008, 7.
[164] Iscaps C. Pets2006 Dataset S7 Camera2[OL], available: http://ftp.cs.rdg.ac.uk/PETS2006/S3-T70-A.zip, 2008, 7.
[165] Pitt M. K., Shephard N. Filtering via simulation: auxiliary particle filter[J]. Journal of the American Statistical Association, 1999, 94(446): 590-599.
[166] Musso C., Oudjane N., Le Gland F. Improving regularized particle filters[M]. In:Sequential Monte Carlo Methods in Practics, Statistics for Engineering and Information Science, New York: Springer-Verlag, 2001.
[167] Kotecha J. H., Djuric P.M. Gaussian Particle Filtering[J]. IEEE Trans. on Signal Processing, 2003, 51(10): 2592-2601.
[168] Texas Instrument Co. TMS320DM642:Video/imaging fixed-point digital signal processor. [OL],available: http://www.ti.com/lit/gpn/tms320dm642, 2007.1.
[169]方帅.计算机智能视频监控系统关键技术研究[博士学位论文].沈阳:东北大学, 2005.
[170] Chang C., Ansari R. Kernel particle filter for visual tracking[J]. IEEE Signal Processing Letters, 2005, 12(3): 242-245.