视频监控若干技术研究
|
摘要
物体遗留和物体移走检测、绊线检测是视频监控中的重要技术。本文综述了国内外物体遗留和物体移走检测、绊线检测的研究现状,设计实现了新的物体遗留和物体移走检测、绊线检测方法。论文完成的主要工作如下:
针对物体遗留和物体移走检测,采用统计模型建立短期背景和长期背景,背景差和相邻帧差相结合提取运动目标,对短期背景非运动区域进行实时更新。每隔? 10?帧将短期背景和长期背景差分,提取目标区域,用边缘特征判定法和巴氏距离判定法相结合的方法识别目标区域是物体遗留还是物体移走。设计开发了具有物体遗留和物体移走检测的监控系统原型,设置不同复杂程度、不同背景的场景,以箱子和包裹作为遗留物体或移走物体,对所设计的物体遗留和物体移走检测方法进行实验验证,结果表明,所设计的方法能够准确实现物体遗留和物体移走检测。
在绊线检测中,采用高斯模型进行背景建模,采用背景差分提取前景目标,扫描绊线上所有点,当检测到目标穿越绊线时,若为双向绊线,系统直接报警,若为单向绊线,利用目标颜色直方图欧拉距离来确定目标运动方向,如果目标运动方向与禁止穿越方向一致,系统报警。设计开发了具有绊线检测的监控系统原型,选择不同的实验场景,设置不同位置和类型的绊线,对所设计的绊线检测方法进行实验验证,结果表明,所设计的方法能够准确实现绊线检测。
Abandoned object detection, removed object detection and trip-line detection are important techniques in video surveillance. Based on the summary of present research situation, novel methods of abandoned object detection, removed object detection and trip-line detection are proposed and testified in this paper. The main works are as follows:
For abandoned object detection and removed object detection, the statistical model is used to establish short-term background and long-term background. Background differencing and coterminous frame differencing are combined to obtain the moving object, and only the non-moving region of short-term background is updated in real-time. The object region is detected by differencing the short-term background and long-term background each 10 frames. When the changing region is detected, edge determinant method and Bhattacharya coefficient determinant method are combined to distinguish whether the changing region is abandoned object or removed object. A surveillance system composed of abandoned object detection and removed object detection is designed and developed in this paper. Different backgrounds are set up by selecting different scenes and artificially interposing jam, where various boxes and packages are selected as abandoned or removed objects. The given abandoned object detection and removed object detection method is verified in several areas. Finally, the experiments testify that the proposed method can detect abandoned and removed objects accurately.
For trip-line detection,multi-Gauss Model is used to model background. Moving objects are detected by background differencing. When foreground object is detected on trip-lines, for double-direction trip-line, system directly alerts while for single-direction trip-line, the moving direction of the object must be analyzed at first. If the moving direction of the object matches forbidden passing rule, system alerts. A surveillance system of trip-line detection is designed and developed. Different backgrounds are set up by selecting different scenes different trip-lines are defined on the scenes, the given trip-line detection method is verified in several areas. The experiments prove the accuracy of the proposed method for trip-line detection.
针对物体遗留和物体移走检测,采用统计模型建立短期背景和长期背景,背景差和相邻帧差相结合提取运动目标,对短期背景非运动区域进行实时更新。每隔? 10?帧将短期背景和长期背景差分,提取目标区域,用边缘特征判定法和巴氏距离判定法相结合的方法识别目标区域是物体遗留还是物体移走。设计开发了具有物体遗留和物体移走检测的监控系统原型,设置不同复杂程度、不同背景的场景,以箱子和包裹作为遗留物体或移走物体,对所设计的物体遗留和物体移走检测方法进行实验验证,结果表明,所设计的方法能够准确实现物体遗留和物体移走检测。
在绊线检测中,采用高斯模型进行背景建模,采用背景差分提取前景目标,扫描绊线上所有点,当检测到目标穿越绊线时,若为双向绊线,系统直接报警,若为单向绊线,利用目标颜色直方图欧拉距离来确定目标运动方向,如果目标运动方向与禁止穿越方向一致,系统报警。设计开发了具有绊线检测的监控系统原型,选择不同的实验场景,设置不同位置和类型的绊线,对所设计的绊线检测方法进行实验验证,结果表明,所设计的方法能够准确实现绊线检测。
Abandoned object detection, removed object detection and trip-line detection are important techniques in video surveillance. Based on the summary of present research situation, novel methods of abandoned object detection, removed object detection and trip-line detection are proposed and testified in this paper. The main works are as follows:
For abandoned object detection and removed object detection, the statistical model is used to establish short-term background and long-term background. Background differencing and coterminous frame differencing are combined to obtain the moving object, and only the non-moving region of short-term background is updated in real-time. The object region is detected by differencing the short-term background and long-term background each 10 frames. When the changing region is detected, edge determinant method and Bhattacharya coefficient determinant method are combined to distinguish whether the changing region is abandoned object or removed object. A surveillance system composed of abandoned object detection and removed object detection is designed and developed in this paper. Different backgrounds are set up by selecting different scenes and artificially interposing jam, where various boxes and packages are selected as abandoned or removed objects. The given abandoned object detection and removed object detection method is verified in several areas. Finally, the experiments testify that the proposed method can detect abandoned and removed objects accurately.
For trip-line detection,multi-Gauss Model is used to model background. Moving objects are detected by background differencing. When foreground object is detected on trip-lines, for double-direction trip-line, system directly alerts while for single-direction trip-line, the moving direction of the object must be analyzed at first. If the moving direction of the object matches forbidden passing rule, system alerts. A surveillance system of trip-line detection is designed and developed. Different backgrounds are set up by selecting different scenes different trip-lines are defined on the scenes, the given trip-line detection method is verified in several areas. The experiments prove the accuracy of the proposed method for trip-line detection.
引文
[1]刘治红,骆云志.智能视频监控技术及其在安防领域的应用[J].兵工自动化.2009, 28(4):75-78.
[2]郑世宝.智能视频监控技术与应用[J].安防科技.2009,3:59-63.
[3] J.Wang and W.Ooi. Detecting static objects in busy scenes [R]. Technical Report TR99-1730, Department of Computer Science, Cornell University, Ithaca, NY 14853-7501, USA.1999.
[4] M.Spengler. and B.Schiele. Automatic detection and tracking of abandoned objects [J]. IEEE Int Work on VS-PETS, Nice, France, Oct. 2003:1-8.
[5] M.Beynon, D.Hook, M.Seibert, A.Peacock and D.Dudgeon. Detecting abandoned packages in a multi-camera video-surveillance system [A]. IEEE International Conference on Advanced Video and Signal-Based Surveillance [C], London UK. 2003:221-228.
[6] E.Auvinet, E.Grossmann, C.Rougier, M.Dahmane and J.Meunier. Left-Luggage Detection using Homographies and Simple Heuristics [A]. Proceedings 9th IEEE International Workshop on PETS [C], New York, June 18, 2006:51-58.
[7] J Martínez-del-Rincón, J. Elías Herrero-Jaraba, J.Raúl Gómez, C.Orrite-Uru uela. Automatic Left Luggage Detection and Tracking Using Multi-Camera UKF[A]. Proceedings 9th IEEE International Workshop on PETS [C], New York, June 18, 2006:59-66.
[8] S.Kevin, P.Quelhas, and D.Gatica-Perez. Detecting Abandoned Luggage Items in a Public Space [A]. Proceedings 9th IEEE International Workshop on PETS [C], New York, June 18, 2006:75-82.
[9] L.Fengjun, S.Xuefeng, W.Bo, Vivek Kumar Singh, Ramakant Nevatia. Left-Luggage Detection using Bayesian Inference [A]. Proceedings 9th IEEE International Workshop on PETS [C], New York, June 18, 2006:83-90.
[10] L.Liyuan, L.Ruijiang, M.Ruihua, H.Weimin, and L.Karianto. Evaluation of An IVS System for Abandoned Object Detection on PETS 2006 Datasets [A]. Proceedings 9th IEEE International Workshop on PETS [C], New York, June 18, 2006:91-98.
[11] S.Guler and K.Farrow. Abandoned Object Detection in Crowded Places [A]. Proceedings 9th IEEE International Workshop on PETS [C], New York, June 18, 2006:99-106.
[12] S.Ferrando, G.Gianluca, C.Regazzoni. Classification of unattended and stolen objects in video surveillance system[A]. Proceedings of IEEE International Conference on Video and Signal Based Surveillance (AVSS’06) [C].2006:21-25.
[13] P.Spagnolo, A.Caroppo, L.Marco. An Abandoned/Removed Objects Detection Algorithm and Its Evaluation on PETS Datasets [A].Proceedings of IEEE International Conference on Advance Video and Signal Based Surveillance (AVSS’06) [C].Washington. DC: IEEE Computer Society, 2006:21-26.
[14] C.Wu-Yu, H.Meng-Fen, H.Chung-Lin, S.T.Lee, Clarence Cheng. Detecting abandoned objects in video-surveillance system [A]. The 21th IPPR Conference on Computer Vision Graphics and Image Processing [C].2008:66-70.
[15] J.C.S.Miguel, J.M. Martínez. Robust unattended and stolen object detection by fusing simple algorithms [A]. IEEE International Conference on Advanced Video and Signal Based Surveillance [C] Santa, USA.2008:18-25.
[16] T.Ying-li, F.Rogerio, A.Hampapur. Real-Time Detection of Abandoned and Removed Objects in Complex Environments [A].The Eighth International Workshop on Visual Surveillance [C] VS2008, Marseille : France (2008) :30-37.
[17] AF.Otoom, H.Dunes, M.Piccardi. Feature Extraction Techniques For Abandoned object Classification In Video Surveillance [A]. The 15th IEEE International Conference on Image Processing [C]. ICIP 2008: 1368-1371.
[18] R.Miezianko and D.Pokrajac. Detecting and Recognizing Abandoned Objects in Crowded Environments [A]. Lecture Notes in Computer Science and Computer Vision Systems[C]. 2008, 5(8): 241-250.
[19] C.Sacchi and S. Regazzoni. A Distributed Surveillance System for Detection of Abandoned Objects in Unmaned Railway Environments [A]. IEEE Transactions on Vehicular Technology[C]. 2008, 49 (5): 2013-2026.
[20]王伟嘉,刘辉,沙莉等.滞留与偷窃物体实时检测与分类算法研究[J].计算机应用.2007,27(10): 2591-2594.
[21]孔英会,张新新,王蕴珠.智能视频监控系统中物体遗留检测方法的研究[J].计算机工程与科学.2010, 6(32): 118-121.
[22]汤一平.基于全方位计算机视觉的遗留物检测装置[P].中国专利: CN101552910, 2009-10-07.
[23]齐明明.视频监控智能分析中移走检测算法的研究[D].北京:北京邮电大学,硕士学位论文.2008.
[24]陈育智.智能视频分析技术在周界报警系统中的应用[J].自动化仪表2009,30(11):14-18.
[25]罗建林.基于IVS的车辆逆行检测技术研究[D].西安:长安大学,硕士学位论文.2008.
[26]代科学,李国辉,涂丹等.监控视频运动目标检测减背景技术的研究现状和展望[J].中国图象图形学报,2006,11(7):919~927.
[27]王陈阳.基于视频的目标检测跟踪的研究与实现[D].西安:西北工业大学,硕士学位论文.2007.
[28]吕行.基于Directshow的智能视频监控系统研究[D].西安:西北工业大学,硕士学位论文.2007.
[29] H.Jwu-Sheng, S.Tzung-Min, J.Shr-Chi. Robust Background Subtraction with Shadow and Highlight [A].Removal for Indoor Surveillance Intelligent Robots and Systems[C], 2006 IEEE/RSJ International Conference on Oct.2006: 4545-4550.
[30] C.Silveira, J.Jacques, C.Rosito. A Background Subtraction Model Adapted to Illumination Changes[A]. Image Processing, 2006 IEEE International Conference[C]. Oct. 2006:1817-1820
[31] R. Cutler, L.Davis. View-based Detection and Analysis of Periodic Motion[J]. 14th International Conference on Pattern Recognition, Brisbane, Australia.1998:495~500.
[32] I.Haritaoglu, D.Harwood, S.Larry. W4:Real-Time surveillance of people and their activities [J]. IEEE Trans. on PAMI (S0162-8828), 2000, 22(8):809-830.
[33] T.Kentaro, J.Krumm, B.Brumitt, B.Meyers. Wallflower: Principles and Practice of Background Maintenance [J]. International Conference on Computer Vision, Corfu, Greece, 1999 9:255-261.
[34] A.Elgammal, D.Harwood, L.Davis. Non-parametric model for background subtraction [J]. In: Proceedings of International Conference on Computer Vision, Kerkyra, Greece, 1999:751~767.
[35] C.Stauffer, W.Eric, L.Grimson. Learning Patterns of Activity Using Real-Time Tracking [J]. IEEE Transactions on pattern analyze and machine intelligence. 2000, 22(8):747-757.
[36] W.Christopher, A.Azarbayejani, T.Darrell, A.Pentland. Pfinder: Real-Time Tracking of the Human Body[J]. IEEE Trans. PAMI.1997,19(7):780-785.
[37] B.Stenger, V.Ramesh, N.Paragios and M. Buhmann. Topology Free Hidden Markov Models: Applicationto Background Modeling [A].In:Proceedings of IEEE International Conference on Computer Vision[C], Vancouver, BC, Canada, 2001, 1:294～301.
[38] N.Oliver, B.Rosario, A.Penfland. A Bayesian computer vision system for modeling human interactions [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2000,22(8): 831～843.
[39] K Fukunage and D.Larry. The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition [J]. IEEE Transactions on Information Theory. 1975, 1(21)32-40.:
[40] K.Kyungnam, H.Thanarat. Chalidabhongse, D.Harwood, L.Davis. Background modeling and subtraction by codebook construction [A]. In: Proceedings of IEEE International Conference on Image Processing [C], Singapore, 2004: 3061 -3064.
[41] T.Horprasert, D.Harwood and S.Davis. A Statistical Approach for Real-time Robust Background Subtraction and Shadow Detection [D]. University of Maryland College Park. Master Degree.1999.
[42]冯形松.基于全方位视觉的运动目标检测跟踪研究[D].杭州:浙江工业大学,硕士学位论文.2009.
[43] J.Barron, D.Fleet, Beauehemin. Performance of optical flow techniques [J]. International Journal of Computer Vision, 1994, 12(l):42-77.
[44] CH Anderson, PJ Burt, and GS Van Der Wal. Change detection and tracking using Pyramid transformation teehniques[J]. In Proceedings of SPIE- Intelligent Robots and Computer Vision, 1985, Vol.579: 72-78.
[45]丁艳.基于全方位视觉的多目标检测跟踪[D].杭州:浙江工业大学,硕士学位论文.2009.
[46] O.Masoud, NP.PapanikoloPoulos. A Novel Method for Tracking and Counting Pedestrians in Real-Time Using a Single Camera [J]. IEEE Transactions on Vehicular Technology, 2001,50(5) : 1267-1278.
[47] N.Otsu.A threshold selection method from gray-1evel histograms[J]. IEEE Transactions on System Man and Cybernetic. 1979,9(1):62-66.
[48]崔屹.图像处理与分析--数学形态学方法及应用[M].科学出版社,2000.
[49]王典.基于混合高斯的背景建模与阴影抑制算法研究[D].西安:西北工业大学,硕士学位论文.2006.
[50] G. D. Finlayson, S. D. Hordley and M. S. Drew. Removing Shadows from Images [A]. In Proc. of European Conf. on Computer Vision[C].2002,4: 823-836.
[51] S. Nadimi and B. Bhnau. Moving shadow detection using a physics-based approach [A]. In Proc. of IEEE International Conference on Patten recognition[C], 2002,2: 701-704.
[52] J.Stauder. Detection of Moving Cast Shadows for Objects Segmentation [A]. IEEE Transactions on Multimedia[C]. 1999, 1(1): 65-76.
[53] R.Cueehiara. Improving Shadow Suppression in Moving Object Detection with HSV Color Information [A]. Proc. of Intel. Transportation Systems Conf[C].2001: 334-339.
[54] N.Martel-Brisson and A.Zaccarin. Moving cast shadow detection from a Gaussian mixture shadow model[A]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition [C]. San Diego, CA, USA,Papers by Session. 2005(2):643-648.
[55] H.Wang and J.A.Schoonees. A re-evaluation of mixture of Gaussian background modeling (video Signal processing applications)[J]. IEEE Proceedings of ICASSP’05. 2005, 2(2): 1017-1020.
[56] I. Mikic, P Cosman, G Kogut and M.M.Trivedi. Moving shadow and object detection in traffic scenes [A]. In Proceedings Of International Conference on Pattern Recognition[C].2000, 9(1):312-324.
[57] E.Salvador, A.Cavallaro and T.Ebrbaimi. Cast shadow segmentation using invariant colore featres. Computer Vision and Image Understanding [J].2004, 95(2): 1077-3142.
[58] E.Salvador, A.Cavallaro and T.Ebrbaimi. Shadow identification and classification using invariant color models[A]. In Proc. IEEE Int. Conf. on Acoustics, Speeeh, and Signal Proeessing[C] (ICASSP). 2001: 1545-1548.
[59] T. Gevers, A.W.M. Smeulders. Color-based object recognition [J], Patten Recogn. 1999(32): 453-464.
[60]李坤伦,孙大跃,赵东生.视频检测的颜色直方图目标匹配算法[J].电脑与信息科技.2008,16(5):7-10.
[61]任建强.交通视频跟踪系统中目标匹配评判方法的研究[J].计算机与数字工程.2009, 37(5):74-76.
[62]彭红霞.智能数字视频监控系统中的若干关键算法研究[D].长沙:国防科技大学,硕士学位论文.2008.
[63]何东健.数字图像处理[M].西安电子科技大学出版社.2003:17-30.
[2]郑世宝.智能视频监控技术与应用[J].安防科技.2009,3:59-63.
[3] J.Wang and W.Ooi. Detecting static objects in busy scenes [R]. Technical Report TR99-1730, Department of Computer Science, Cornell University, Ithaca, NY 14853-7501, USA.1999.
[4] M.Spengler. and B.Schiele. Automatic detection and tracking of abandoned objects [J]. IEEE Int Work on VS-PETS, Nice, France, Oct. 2003:1-8.
[5] M.Beynon, D.Hook, M.Seibert, A.Peacock and D.Dudgeon. Detecting abandoned packages in a multi-camera video-surveillance system [A]. IEEE International Conference on Advanced Video and Signal-Based Surveillance [C], London UK. 2003:221-228.
[6] E.Auvinet, E.Grossmann, C.Rougier, M.Dahmane and J.Meunier. Left-Luggage Detection using Homographies and Simple Heuristics [A]. Proceedings 9th IEEE International Workshop on PETS [C], New York, June 18, 2006:51-58.
[7] J Martínez-del-Rincón, J. Elías Herrero-Jaraba, J.Raúl Gómez, C.Orrite-Uru uela. Automatic Left Luggage Detection and Tracking Using Multi-Camera UKF[A]. Proceedings 9th IEEE International Workshop on PETS [C], New York, June 18, 2006:59-66.
[8] S.Kevin, P.Quelhas, and D.Gatica-Perez. Detecting Abandoned Luggage Items in a Public Space [A]. Proceedings 9th IEEE International Workshop on PETS [C], New York, June 18, 2006:75-82.
[9] L.Fengjun, S.Xuefeng, W.Bo, Vivek Kumar Singh, Ramakant Nevatia. Left-Luggage Detection using Bayesian Inference [A]. Proceedings 9th IEEE International Workshop on PETS [C], New York, June 18, 2006:83-90.
[10] L.Liyuan, L.Ruijiang, M.Ruihua, H.Weimin, and L.Karianto. Evaluation of An IVS System for Abandoned Object Detection on PETS 2006 Datasets [A]. Proceedings 9th IEEE International Workshop on PETS [C], New York, June 18, 2006:91-98.
[11] S.Guler and K.Farrow. Abandoned Object Detection in Crowded Places [A]. Proceedings 9th IEEE International Workshop on PETS [C], New York, June 18, 2006:99-106.
[12] S.Ferrando, G.Gianluca, C.Regazzoni. Classification of unattended and stolen objects in video surveillance system[A]. Proceedings of IEEE International Conference on Video and Signal Based Surveillance (AVSS’06) [C].2006:21-25.
[13] P.Spagnolo, A.Caroppo, L.Marco. An Abandoned/Removed Objects Detection Algorithm and Its Evaluation on PETS Datasets [A].Proceedings of IEEE International Conference on Advance Video and Signal Based Surveillance (AVSS’06) [C].Washington. DC: IEEE Computer Society, 2006:21-26.
[14] C.Wu-Yu, H.Meng-Fen, H.Chung-Lin, S.T.Lee, Clarence Cheng. Detecting abandoned objects in video-surveillance system [A]. The 21th IPPR Conference on Computer Vision Graphics and Image Processing [C].2008:66-70.
[15] J.C.S.Miguel, J.M. Martínez. Robust unattended and stolen object detection by fusing simple algorithms [A]. IEEE International Conference on Advanced Video and Signal Based Surveillance [C] Santa, USA.2008:18-25.
[16] T.Ying-li, F.Rogerio, A.Hampapur. Real-Time Detection of Abandoned and Removed Objects in Complex Environments [A].The Eighth International Workshop on Visual Surveillance [C] VS2008, Marseille : France (2008) :30-37.
[17] AF.Otoom, H.Dunes, M.Piccardi. Feature Extraction Techniques For Abandoned object Classification In Video Surveillance [A]. The 15th IEEE International Conference on Image Processing [C]. ICIP 2008: 1368-1371.
[18] R.Miezianko and D.Pokrajac. Detecting and Recognizing Abandoned Objects in Crowded Environments [A]. Lecture Notes in Computer Science and Computer Vision Systems[C]. 2008, 5(8): 241-250.
[19] C.Sacchi and S. Regazzoni. A Distributed Surveillance System for Detection of Abandoned Objects in Unmaned Railway Environments [A]. IEEE Transactions on Vehicular Technology[C]. 2008, 49 (5): 2013-2026.
[20]王伟嘉,刘辉,沙莉等.滞留与偷窃物体实时检测与分类算法研究[J].计算机应用.2007,27(10): 2591-2594.
[21]孔英会,张新新,王蕴珠.智能视频监控系统中物体遗留检测方法的研究[J].计算机工程与科学.2010, 6(32): 118-121.
[22]汤一平.基于全方位计算机视觉的遗留物检测装置[P].中国专利: CN101552910, 2009-10-07.
[23]齐明明.视频监控智能分析中移走检测算法的研究[D].北京:北京邮电大学,硕士学位论文.2008.
[24]陈育智.智能视频分析技术在周界报警系统中的应用[J].自动化仪表2009,30(11):14-18.
[25]罗建林.基于IVS的车辆逆行检测技术研究[D].西安:长安大学,硕士学位论文.2008.
[26]代科学,李国辉,涂丹等.监控视频运动目标检测减背景技术的研究现状和展望[J].中国图象图形学报,2006,11(7):919~927.
[27]王陈阳.基于视频的目标检测跟踪的研究与实现[D].西安:西北工业大学,硕士学位论文.2007.
[28]吕行.基于Directshow的智能视频监控系统研究[D].西安:西北工业大学,硕士学位论文.2007.
[29] H.Jwu-Sheng, S.Tzung-Min, J.Shr-Chi. Robust Background Subtraction with Shadow and Highlight [A].Removal for Indoor Surveillance Intelligent Robots and Systems[C], 2006 IEEE/RSJ International Conference on Oct.2006: 4545-4550.
[30] C.Silveira, J.Jacques, C.Rosito. A Background Subtraction Model Adapted to Illumination Changes[A]. Image Processing, 2006 IEEE International Conference[C]. Oct. 2006:1817-1820
[31] R. Cutler, L.Davis. View-based Detection and Analysis of Periodic Motion[J]. 14th International Conference on Pattern Recognition, Brisbane, Australia.1998:495~500.
[32] I.Haritaoglu, D.Harwood, S.Larry. W4:Real-Time surveillance of people and their activities [J]. IEEE Trans. on PAMI (S0162-8828), 2000, 22(8):809-830.
[33] T.Kentaro, J.Krumm, B.Brumitt, B.Meyers. Wallflower: Principles and Practice of Background Maintenance [J]. International Conference on Computer Vision, Corfu, Greece, 1999 9:255-261.
[34] A.Elgammal, D.Harwood, L.Davis. Non-parametric model for background subtraction [J]. In: Proceedings of International Conference on Computer Vision, Kerkyra, Greece, 1999:751~767.
[35] C.Stauffer, W.Eric, L.Grimson. Learning Patterns of Activity Using Real-Time Tracking [J]. IEEE Transactions on pattern analyze and machine intelligence. 2000, 22(8):747-757.
[36] W.Christopher, A.Azarbayejani, T.Darrell, A.Pentland. Pfinder: Real-Time Tracking of the Human Body[J]. IEEE Trans. PAMI.1997,19(7):780-785.
[37] B.Stenger, V.Ramesh, N.Paragios and M. Buhmann. Topology Free Hidden Markov Models: Applicationto Background Modeling [A].In:Proceedings of IEEE International Conference on Computer Vision[C], Vancouver, BC, Canada, 2001, 1:294～301.
[38] N.Oliver, B.Rosario, A.Penfland. A Bayesian computer vision system for modeling human interactions [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2000,22(8): 831～843.
[39] K Fukunage and D.Larry. The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition [J]. IEEE Transactions on Information Theory. 1975, 1(21)32-40.:
[40] K.Kyungnam, H.Thanarat. Chalidabhongse, D.Harwood, L.Davis. Background modeling and subtraction by codebook construction [A]. In: Proceedings of IEEE International Conference on Image Processing [C], Singapore, 2004: 3061 -3064.
[41] T.Horprasert, D.Harwood and S.Davis. A Statistical Approach for Real-time Robust Background Subtraction and Shadow Detection [D]. University of Maryland College Park. Master Degree.1999.
[42]冯形松.基于全方位视觉的运动目标检测跟踪研究[D].杭州:浙江工业大学,硕士学位论文.2009.
[43] J.Barron, D.Fleet, Beauehemin. Performance of optical flow techniques [J]. International Journal of Computer Vision, 1994, 12(l):42-77.
[44] CH Anderson, PJ Burt, and GS Van Der Wal. Change detection and tracking using Pyramid transformation teehniques[J]. In Proceedings of SPIE- Intelligent Robots and Computer Vision, 1985, Vol.579: 72-78.
[45]丁艳.基于全方位视觉的多目标检测跟踪[D].杭州:浙江工业大学,硕士学位论文.2009.
[46] O.Masoud, NP.PapanikoloPoulos. A Novel Method for Tracking and Counting Pedestrians in Real-Time Using a Single Camera [J]. IEEE Transactions on Vehicular Technology, 2001,50(5) : 1267-1278.
[47] N.Otsu.A threshold selection method from gray-1evel histograms[J]. IEEE Transactions on System Man and Cybernetic. 1979,9(1):62-66.
[48]崔屹.图像处理与分析--数学形态学方法及应用[M].科学出版社,2000.
[49]王典.基于混合高斯的背景建模与阴影抑制算法研究[D].西安:西北工业大学,硕士学位论文.2006.
[50] G. D. Finlayson, S. D. Hordley and M. S. Drew. Removing Shadows from Images [A]. In Proc. of European Conf. on Computer Vision[C].2002,4: 823-836.
[51] S. Nadimi and B. Bhnau. Moving shadow detection using a physics-based approach [A]. In Proc. of IEEE International Conference on Patten recognition[C], 2002,2: 701-704.
[52] J.Stauder. Detection of Moving Cast Shadows for Objects Segmentation [A]. IEEE Transactions on Multimedia[C]. 1999, 1(1): 65-76.
[53] R.Cueehiara. Improving Shadow Suppression in Moving Object Detection with HSV Color Information [A]. Proc. of Intel. Transportation Systems Conf[C].2001: 334-339.
[54] N.Martel-Brisson and A.Zaccarin. Moving cast shadow detection from a Gaussian mixture shadow model[A]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition [C]. San Diego, CA, USA,Papers by Session. 2005(2):643-648.
[55] H.Wang and J.A.Schoonees. A re-evaluation of mixture of Gaussian background modeling (video Signal processing applications)[J]. IEEE Proceedings of ICASSP’05. 2005, 2(2): 1017-1020.
[56] I. Mikic, P Cosman, G Kogut and M.M.Trivedi. Moving shadow and object detection in traffic scenes [A]. In Proceedings Of International Conference on Pattern Recognition[C].2000, 9(1):312-324.
[57] E.Salvador, A.Cavallaro and T.Ebrbaimi. Cast shadow segmentation using invariant colore featres. Computer Vision and Image Understanding [J].2004, 95(2): 1077-3142.
[58] E.Salvador, A.Cavallaro and T.Ebrbaimi. Shadow identification and classification using invariant color models[A]. In Proc. IEEE Int. Conf. on Acoustics, Speeeh, and Signal Proeessing[C] (ICASSP). 2001: 1545-1548.
[59] T. Gevers, A.W.M. Smeulders. Color-based object recognition [J], Patten Recogn. 1999(32): 453-464.
[60]李坤伦,孙大跃,赵东生.视频检测的颜色直方图目标匹配算法[J].电脑与信息科技.2008,16(5):7-10.
[61]任建强.交通视频跟踪系统中目标匹配评判方法的研究[J].计算机与数字工程.2009, 37(5):74-76.
[62]彭红霞.智能数字视频监控系统中的若干关键算法研究[D].长沙:国防科技大学,硕士学位论文.2008.
[63]何东健.数字图像处理[M].西安电子科技大学出版社.2003:17-30.