运动目标检测算法研究
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摘要
在现实生活中,运动图像包含有大量有意义的视觉信息。运动目标检测技术作为计算机视觉、运动图像分析、人机交互和智能视频监控技术的基础已广泛应用于军事、计算机辅助设计、航空航天、智能机器人等领域,其检测结果的好坏直接影响到目标定位、跟踪以及行为理解等后续处理。因此运动目标检测技术的研究有很重要的理论价值和实际应用意义。本文主要研究背景相对静止下的智能视频监控系统中运动目标检测算法,主要工作包括:
1.介绍了一些图像预处理方面的相关技术,包括:颜色空间模型、图像去噪、图像均衡化、图像边缘检测、图像形态学处理,并对各项技术进行了实验验证。
2.简要介绍了图像阈值分割的相关方法。详细介绍了基于阈值分割的帧间差分法和背景差分法两种运动目标检测算法,包括各算法的原理和优缺点,并对各算法进行实验验证。针对两种算法的不足分别提出了改进算法:基于帧间差分和图像边界信息的运动目标检测算法,以及基于IIR滤波器的背景更新运动目标检测算法。另外,简要介绍了另一种常用的运动目标检测算法——光流法。
3.为了解决当目标运动较慢或尺寸较小时易出现漏检的问题,在现有运动目标检测方法的基础上,提出了一种将时域和空域结合的运动目标检测方法,即运动信息和标记多尺度分水岭相结合的运动目标检测算法。该方法以时域信息为基础,结合空域的分水岭分割方法可以得到良好的运动目标检测结果,算法具有快速性和鲁棒性。
In real life, moving images contain a large number of meaningful visual information. Moving object detection is the base of computer vision, motion, image analysis, human-computer interaction and intelligent video surveillance technology, which is widely used in the military, computer-aided design, aerospace, intelligent robotics and other fields, the test results have a direct impact to the follow-up treatment such as target location, tracking and behavior understanding. So moving object detection technology has a very important theoretical and practical application of significance. This thesis does research on the moving object detection in the background of a relatively stationary video surveillance system, mainly include:
1. Describes some aspects of image pre-processing technologies, including: color space model, image denoising, image equalization, image edge detection, image morphological processing, and experimental validation of the technology.
2. Briefly describes the image segmentation method, with fully descriptions for two kinds of moving object detection algorithm: frame difference and background difference, which are base of the threshold segmentation, including the principle of two object detection algorithm, besides the advantages and disadvantages between this two, and making the experimental verification of the algorithm. Considering the disadvantages of two object detection algorithms proposes some improved algorithms, which include motion detection algorithm based on Inter-frame difference method and edge information, and background update algorithm based on IIR filter. In addition, briefly introduces the knowledge of optical flow method, which is another commonly used algorithm for moving object detection.
3. In order to solving the problems that easily lack to check when the object running slower or the size is smaller, proposes a method of motion detection combining time domain and spatial, base on existing method of moving object detection, which is the motion detection algorithm that combines of motion information and marker multi-measure watershed. This method based on information in the time domain, can get good results of moving object detection that combined with the watershed segmentation method. The result shows that the detection algorithm can detect object accurately and quickly.
1.介绍了一些图像预处理方面的相关技术,包括:颜色空间模型、图像去噪、图像均衡化、图像边缘检测、图像形态学处理,并对各项技术进行了实验验证。
2.简要介绍了图像阈值分割的相关方法。详细介绍了基于阈值分割的帧间差分法和背景差分法两种运动目标检测算法,包括各算法的原理和优缺点,并对各算法进行实验验证。针对两种算法的不足分别提出了改进算法:基于帧间差分和图像边界信息的运动目标检测算法,以及基于IIR滤波器的背景更新运动目标检测算法。另外,简要介绍了另一种常用的运动目标检测算法——光流法。
3.为了解决当目标运动较慢或尺寸较小时易出现漏检的问题,在现有运动目标检测方法的基础上,提出了一种将时域和空域结合的运动目标检测方法,即运动信息和标记多尺度分水岭相结合的运动目标检测算法。该方法以时域信息为基础,结合空域的分水岭分割方法可以得到良好的运动目标检测结果,算法具有快速性和鲁棒性。
In real life, moving images contain a large number of meaningful visual information. Moving object detection is the base of computer vision, motion, image analysis, human-computer interaction and intelligent video surveillance technology, which is widely used in the military, computer-aided design, aerospace, intelligent robotics and other fields, the test results have a direct impact to the follow-up treatment such as target location, tracking and behavior understanding. So moving object detection technology has a very important theoretical and practical application of significance. This thesis does research on the moving object detection in the background of a relatively stationary video surveillance system, mainly include:
1. Describes some aspects of image pre-processing technologies, including: color space model, image denoising, image equalization, image edge detection, image morphological processing, and experimental validation of the technology.
2. Briefly describes the image segmentation method, with fully descriptions for two kinds of moving object detection algorithm: frame difference and background difference, which are base of the threshold segmentation, including the principle of two object detection algorithm, besides the advantages and disadvantages between this two, and making the experimental verification of the algorithm. Considering the disadvantages of two object detection algorithms proposes some improved algorithms, which include motion detection algorithm based on Inter-frame difference method and edge information, and background update algorithm based on IIR filter. In addition, briefly introduces the knowledge of optical flow method, which is another commonly used algorithm for moving object detection.
3. In order to solving the problems that easily lack to check when the object running slower or the size is smaller, proposes a method of motion detection combining time domain and spatial, base on existing method of moving object detection, which is the motion detection algorithm that combines of motion information and marker multi-measure watershed. This method based on information in the time domain, can get good results of moving object detection that combined with the watershed segmentation method. The result shows that the detection algorithm can detect object accurately and quickly.
引文
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[11]华中科技大学图像识别与人工智能研究所. http://iprai.hust.edu.cn/index.aspx.
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[2] G. Storvik. A Bayesian approach to dynamic contours through stochastic sampling and simulated Annealing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(10):970-986.
[3] R. Cole, L. Hirschman, et al. The challenge of spoken language systems: Research directions for the nineties[J].IEEE Transactions on Speech and Audio Processing, 1995, 3(1):1-21.
[4] A. Pentland. Smart rooms[J].Scientific American, 1996, 274(4):68-76.
[5] T. Kanade, R. Collins, A. Lipton, et al. Advances in cooperative multi-sensor video surveillance[C]. Proceedings DARPA Image Understanding Workshop, Morgan kaufmann: DARPA, 1998. 3-4.
[6] Y. Onoe, N. Yokoya, K. Yamazawa, et al. Visual surveillance and monitoring system using an omnidirectional video camera[C]. 14th International Conference on Pattern Recognition, Brisbane, Australia, 1998:588-592.
[7] I. Haritaoglu, D. Harwood, L. S. Davis. W4:Real-time surveillance of people and their activities[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8):809-830.
[8] A. J. Schofield, P. A. Mehta, T. J. Stonham. A system for counting people in video images using neural networks to identify the background Scene[J]. Pattern Recognition, 1996, 29(8): 1421-1428.
[9] J. D. Prange. Detecting, recognizing and understanding video events in surveillance video[C]. Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, Miami:AVSS’03,2003: 48-57.
[10]中国科学院自动化研究所. http://www.ia.cas.cn/kygz/kycg/hjcg/.
[11]华中科技大学图像识别与人工智能研究所. http://iprai.hust.edu.cn/index.aspx.
[12] Liang Wang, Tieniu Tan, Huazhong Ning, Weiming Hu. Silhoutte analysis based gaitrecognition for human identification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(12): 1505-1518.
[13] B. K. P. Horn, B. G. Schunck. Determining optical flow[J]. Artificial Intelligence, 1981, 17(3):185-203.
[14] J. L. Barron, D. J. Fleet, S. S. Beauchemin. Performance of optical flow techniques[J]. International Journal of Computer Vision, 1994, 12(1):43-47.
[15] E. Holden, R. Owens. Segmenting occluded objects using a motion snake[C]. Asian Conference on Computer Vision, Korea: ACCV, 2004, SIV-3.
[16] A. A. Efros, A. C. Berg, G. Mori, et al. Recognizing action at a distance[C]. 9th IEEE International Conference on Computer Vision, Nice, France: ICCV, 2003: 726-733.
[17] B. D. Lucas, T. Kanade. An iterative image registration technique with an application to stereo vision[C]. Proceedings of The 7th International Joint Conference on Artificial Intelligence, 1981, 10:674-679.
[18] H. H. Nagel. Displacement vectors derived from second-order intensity variations in image sequences[J]. Computer Vision, Graphics, and Image Processing, 1983, 21(1):85-117.
[19] A. J. Lipton, H. Fujiyoshi. Moving target classification and tracking from real-time video[C]. Proc IEEE Workshop on Applications of Computer, Princeton, NJ, 1998:8-14.
[20] J. Rymal, J. Renno, D. Greenhill, et a1. Adaptive eigen-backgrounds for object detection[C]. The IEEE International Conference on Imge Proeessing(JCIP), Kingston Univ, UK, 2004.
[21] N. Amamoto, A. Fujii. Detection obstructions and tracking moving objects by image processing technique[C]. Electronic Communication, Japan: 1999, 10(11):527-535.
[22]杨威,张田文.复杂景物环境下运动目标检测的新方法[J].计算机研究与发展, 1998, 35(8):724-728.
[23] Du Yuren, Zhou Aijun, Yuan Feng. Moving object detection for video monitoring systems[C]. The 8th International Conference on Electronic Measurement and Instruments, 2007: 814-817.
[24] Zhan Chaohui, Duan Xiaohui et al. An improved moving object detection algorithm based on frame difference and edge detection[C]. 4th International Conference on Image and Graphics, 2007, 12(2): 519-523.
[25]刘翔.基于视频的运动目标检测与跟踪算法研究与应用[D].武汉:武汉科技大学, 2009.
[26]陈敏.智能监控系统中运动检测与跟踪方法的研究[D].武汉:武汉科技大学, 2008.
[27]陈奕奕.运动目标检测分割算法研究[D].武汉:武汉科技大学, 2010.
[28] Gonzalez, R. C, Woods, R. E,阮秋琦,阮宇智译.数学图像处理(第二版) [M].北京:电子工业出版社, 2007.
[29]杨高波,杜青松. MATLAB图像\视频处理应用及实例[M].北京:电子工业出版社, 2010: 35-38.
[30]刘俊.运动目标的检测与跟踪算法研究[D].武汉:武汉科技大学, 2011.
[31]吴晓阳.基于OpenCV的运动目标检测与跟踪[D].杭州:浙江大学, 2008.
[32] Dong Yiqiu, Xu Shufang. A new directional weighted median filter for removal of random-valued impulse noise[J]. IEEE Signal Processing Letters, 2001, 14(3): 193-196.
[33]秦鹏,丁润涛.一种基于排序阈值的开关中值滤波方法[J].中国图象图形学报, 2004, 4: 412-416.
[34]赵高长,张磊,武风波.改进的中值滤波算法在图像去噪中的应用[J].应用光学, 2011, 4(32): 678-456.
[35]张懿,刘旭,李海峰.自适应图像直方图均衡算法[J].浙江大学学报(工学版), 2007, 4(41): 630-633.
[36]赵书兰. MATLAB R2008数字图像处理与分析实例教程[M].北京:化学工业出版社, 2009: 138-139.
[37] Gonzalez, R. C, Woods, R., Eddins, S. L,阮秋琦等译.数学图像处理(MATLAB版)[M].北京:电子工业出版社, 2008.
[38]魏伟波,芮筱亭.图像边缘检测方法研究[J].计算机工程与应用, 2006(30): 88-91.
[39]李安安.几种图像边缘检测算法的比较与展望[J].大众科技, 2009, 12(124): 46-47.
[40]章毓晋著.图像工程(下册)图像理解(第二版) [M].北京:清华大学出版社, 2007: 108-141.
[41]韩青松,贾振红,杨杰,庞韶宁.基于改进的Otsu算法的遥感图像阈值分割[J].激光杂志, 2010, 6: 33-34.
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