视频中的运动目标检测与跟踪算法研究
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摘要
计算机视觉领域中的运动目标检测与跟踪,是从视频或图像序列数据中检测出运动对象并进行持续追踪,它为高层的视频理解提供基础元素和分析依据。本文研究并实现了视频中利用帧间差分算法和背景差算法进行运动目标检测,分析了各种检测方法的优缺点。利用Kalman滤波器的原理,结合波门跟踪,对所采集的视频进行了单个目标跟踪实验,跟踪效果良好;针对同一个视频中多个运动目标的跟踪问题,本文利用SIFT算法对感兴趣运动目标进行锁定跟踪实验,结果表明,当有多个运动目标出现时,对感兴趣的目标均有良好的跟踪效果。
The task of moving object detecting and tracking in the computer vision system is to detect the moving target in the in video sequence or image sequence and keep tracking. It provides basic elements and analysis foundation for the high-level understanding of videos.In this paper, the detecting of moving target was reseached and achieved using the methods of inter-frame difference and background difference. It also analyzed the advantages and disadvantages of various detection methods. The tracking of moving target was accomplished by the method using Kalman filter which combined the gate-tracking principle, and got a good result in the experiment from the collected video with a single target . An algorithm based on SIFT algorithm for moving target tracking is proposed in this paper. The algorithm sovled the problem in the tracking target when multiple moving objects appreared in one video. The result showed that tracking one target in the video scene where multiple moving targets appeared was achieved.
The task of moving object detecting and tracking in the computer vision system is to detect the moving target in the in video sequence or image sequence and keep tracking. It provides basic elements and analysis foundation for the high-level understanding of videos.In this paper, the detecting of moving target was reseached and achieved using the methods of inter-frame difference and background difference. It also analyzed the advantages and disadvantages of various detection methods. The tracking of moving target was accomplished by the method using Kalman filter which combined the gate-tracking principle, and got a good result in the experiment from the collected video with a single target . An algorithm based on SIFT algorithm for moving target tracking is proposed in this paper. The algorithm sovled the problem in the tracking target when multiple moving objects appreared in one video. The result showed that tracking one target in the video scene where multiple moving targets appeared was achieved.
引文
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[18]Zhu Z., Xu G., Yang B., Shi D., Lin X. VISATRAM:A real-time vision system for automatic traffic monitoring. Image and Vision Computing, 2000, 18(10):781-794
[19]Hang M., Nagel H. Tracking of complex driving maneuvers in traffic image sequences Image and Vision Computing, 1998, 16(8):517-527
[20]Avidan S. Support vector tracking. Proc. of IEEE Conf. Computer Vision and Pattern Recognition, 2001, Vol. 1, 184-191
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[22]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
[23]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
[24]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
[25]S Li, Z Zhang. FloatBoost Learning and Statistical Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(9):1122-1123
[26]L Wang, T Tan, H Ning, et al. Fusion of Satatic and Dynamic Body Biometrics for Gait Rccognition. IEEE Transactions on Circuits and Systems for VideoTechnology, 2004, 14(2):149-158
[27] Y Fang, Y Wang, T Tan. Inproving Face Detection through Fusion of Contour and Region Information. Chinese Journal of Computer, 2004, 27(4):482-491
[28]H Liu, Y wang, T Tan. Multi-Modal Data Fusion for person Authentication using Improved ENN. Chinese Joumal of Automation, 2004, 30(l):78-85
[29]C Xu, Y Wang, T Tan. Overview of Research on 3D Faee Modeling. Journal of Image and Graphics, 2004, 9(8):893-903
[30]Zhu Z. G., Xu G. Y. VISATRAM:a real-time vision system for automatic traffic monitoring. Image and Vision Computing. 2000, 18(10):781-794
[31]Pai C. -J., Tyan H. -R., Liang Y. -M., et al. Pedestrian detection and tracking at crossroads. Pattern Recognition. 2004, 37(5):1025-1034
[32]Betke M., Haritaoglu E., Davis L. S. Real-time multiple vehicle detection and trackingfrom a moving vehicle. Machine Vision and Applications. 2000, 12 (2):69-83
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[35]Babu R. V., Patrick P., rez, et al. Robust tracking with motion estimation and local Kernel-based color modeling. Image Vision Computing. 2007, 25(8): 1205-1216
[36]Elgammal A., Duraiswami R., Harwood D., et al. Background and foregroundmodeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE. 2002, 90(7):1151-1163
[37]Gavrila D. M. The visual analysis of human movement:a survey. Computer Vision and Image Understanding. 1999, 73(1):82-98
[38]Alberto S., Juan J. V. An approach of visual motion analysis. Pattern Recognition Letters. 2005, 26(3):355-368
[39]Pentland A. Looking at people:sensing for ubiquitous and wearable computing. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000, 22(1):107-119
[40]Francisco B. -F., Alberto O., Gabriel O. Visual Navigation for Mobile Robots: A Survey. Journal of Intelligent and Robotic Systems. 2008, 53(3):263-296
[41]Papanikolopoulos N. P., Khosla P. K., Kanade T. Visual tracking of a moving target by a camera mounted on a robot:a combination of control and vision. IEEE Transactions on Robotics and Automation. 1993, 9(1):14-35
[42]Li T. H. S., Chang S. -J. Autonomous fuzzy parking control of a car-like mobile robot. IEEE Transactions on Systems, Man and Cybernetics. 2003, 33 (4):451-465
[43]姚秀娟,彭晓乐,张永科.几种精确制导技术简述.激光与红外.2006,36 (5):338-340
[44]王小卫,宁固.一种改进的基于光流的运动目标的检测算法.武汉大学学报信息科学版,2003,(3):101-103
[45]孙正,郁道银,陈晓冬等.基于光流法的冠状动脉造影图像序列中血管运动的估计.工程学学报,2003,(3):91-96
[46]李超,熊璋,赫阳等.基于帧间差的区域光流分析及其应用.计算机工程与应用. 2005,(31):199-201
[47]章毓晋.图像工程(第2版).北京,清华大学出版社.2007
[48]冯伟.视频序列运动目标检测与识别方法研究.[硕士学位论文].西安:西北工业大学,2003
[49]谭优,王泽勇.图像阈值分割算法实用技术研究与比较.微计算机信息,2007, 23(24):298-233
[50]Hsu C. -T., Hsieh S. -M. Region Tracking for Non-Rigid Video Objects in a Non-Parametric MAP Framework. Signal Processing:Image Communication. 2006, 21(3):235-251
[51]宋新,罗军,王鲁平等.基于GVF Snake的运动目标跟踪方法.红外与激光工程.2006.35(1):111-116
[52]MoravecHP. Towards automatic visual obstacle avoidance. In: Proceedings of International Joint conference on Artificial Intelligence, cambridge, MA, USA, 1977: 584-590
[53]Chris Harris, Mike Stephens. A combined Corner and Edge Detecto. Manchester Proceedings of the 4th A1vey Vision Conference, 1988: 147-151
[2]D.Marr.视觉计算理论.北京:科学出版社,1998,147-238
[3]A.Lipton, H Fujiyoshi and R Patil. Moving Targer Classification and Tracking from Real-Time Video. Proc. of the 1998 DARPA Image Understanding Worshop(IUW’98), 1998
[4]Yuting Chen, Chusong Chen, Chunrong Huang and Yiping Hong, Effecint hierarchical methd for background subtraction Pattern Recognition, Inpress, Corrected Proof, Available online 16 January 2007
[5]MaKenna S, JabriS, Duric Z, Rosenfeld A, Wechsler H. Tracking Groups Of People. Computer Vision and Image Understanding, 2000, 80(1):42-56
[6]Arseneau. S, CooPerstock J. Real-Time Image Segmentation for Action Recognition. Proc. IEEE Pacific Rim Conference on Communication, Compters and Signal Processing, Victoria, Canada, 1999, 86-89
[7]SunH, Feng T, Tan T. Robust extraction of moving objects from image sequences In: Proc the Fourih Asian Conference on Computer Vision, Taiwan, 2000, 961-964
[8]张江山,朱光喜.一种基于Kalman滤波的视频对象跟踪方法.中国图像图报.Vol. 7(A),NO. 6
[9]Barron J., Fleet D., Beauchemin S. Performance of optical flow techniques. International Journal of Computer Vision, 1994, 12(1):42-77
[10]张海青,李厚强.基于蒙特卡罗方法的目标跟踪.中国图象图形学报.2008, 13(5):937-943
[11]T. Huang, S. Russell, Object identification:A Bayesian analysis with application to traffic surveillance, Artificial Intelligence, 1998, (103):l-17
[12] W. Leoputra, T, tan, F. L. Lim. Noc-overlapping Distributed Tracking Using Partcle Filter, In Proc. Of the International Conference on Pattern Recognition. 2006, 181-185
[13]C. Motamed, O. Wallart, A temporal fusion strategy for cross-camera data association. Pattern Recognition Letters, 2007, (28):233-245
[14]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
[15]Collins R., Lipton A. and Kanade T. Introduction to the special section on video surveillance. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2000, 22(8):745-746
[16]Coifman B., Beymer D., Mclauchlan P., Malik J. A real-time computer vision system for vehicle tracking and traffic surveillance. Transportation Research Part C, 1998, 6(4):271-288
[17]Tai J., Tsang S., Lin C., Song K. Real-time image tracking for automatic traffic monitoring and enforment application, Image and Vision Computing, 2004, 22(6):485-501
[18]Zhu Z., Xu G., Yang B., Shi D., Lin X. VISATRAM:A real-time vision system for automatic traffic monitoring. Image and Vision Computing, 2000, 18(10):781-794
[19]Hang M., Nagel H. Tracking of complex driving maneuvers in traffic image sequences Image and Vision Computing, 1998, 16(8):517-527
[20]Avidan S. Support vector tracking. Proc. of IEEE Conf. Computer Vision and Pattern Recognition, 2001, Vol. 1, 184-191
[21]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
[22]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
[23]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
[24]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
[25]S Li, Z Zhang. FloatBoost Learning and Statistical Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(9):1122-1123
[26]L Wang, T Tan, H Ning, et al. Fusion of Satatic and Dynamic Body Biometrics for Gait Rccognition. IEEE Transactions on Circuits and Systems for VideoTechnology, 2004, 14(2):149-158
[27] Y Fang, Y Wang, T Tan. Inproving Face Detection through Fusion of Contour and Region Information. Chinese Journal of Computer, 2004, 27(4):482-491
[28]H Liu, Y wang, T Tan. Multi-Modal Data Fusion for person Authentication using Improved ENN. Chinese Joumal of Automation, 2004, 30(l):78-85
[29]C Xu, Y Wang, T Tan. Overview of Research on 3D Faee Modeling. Journal of Image and Graphics, 2004, 9(8):893-903
[30]Zhu Z. G., Xu G. Y. VISATRAM:a real-time vision system for automatic traffic monitoring. Image and Vision Computing. 2000, 18(10):781-794
[31]Pai C. -J., Tyan H. -R., Liang Y. -M., et al. Pedestrian detection and tracking at crossroads. Pattern Recognition. 2004, 37(5):1025-1034
[32]Betke M., Haritaoglu E., Davis L. S. Real-time multiple vehicle detection and trackingfrom a moving vehicle. Machine Vision and Applications. 2000, 12 (2):69-83
[33]Hu W., Tan T., Wang L., et al. A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews. 2004, 34(3):334-352
[34]Mohamed F. A., Rama C., Zheng Q. Integrated motion detection and tracking for visual surveillance, Proceedings of the Fourth IEEE International Conference on Computer Vision System(ICVS). New York:IEEE, 2006:28
[35]Babu R. V., Patrick P., rez, et al. Robust tracking with motion estimation and local Kernel-based color modeling. Image Vision Computing. 2007, 25(8): 1205-1216
[36]Elgammal A., Duraiswami R., Harwood D., et al. Background and foregroundmodeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE. 2002, 90(7):1151-1163
[37]Gavrila D. M. The visual analysis of human movement:a survey. Computer Vision and Image Understanding. 1999, 73(1):82-98
[38]Alberto S., Juan J. V. An approach of visual motion analysis. Pattern Recognition Letters. 2005, 26(3):355-368
[39]Pentland A. Looking at people:sensing for ubiquitous and wearable computing. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000, 22(1):107-119
[40]Francisco B. -F., Alberto O., Gabriel O. Visual Navigation for Mobile Robots: A Survey. Journal of Intelligent and Robotic Systems. 2008, 53(3):263-296
[41]Papanikolopoulos N. P., Khosla P. K., Kanade T. Visual tracking of a moving target by a camera mounted on a robot:a combination of control and vision. IEEE Transactions on Robotics and Automation. 1993, 9(1):14-35
[42]Li T. H. S., Chang S. -J. Autonomous fuzzy parking control of a car-like mobile robot. IEEE Transactions on Systems, Man and Cybernetics. 2003, 33 (4):451-465
[43]姚秀娟,彭晓乐,张永科.几种精确制导技术简述.激光与红外.2006,36 (5):338-340
[44]王小卫,宁固.一种改进的基于光流的运动目标的检测算法.武汉大学学报信息科学版,2003,(3):101-103
[45]孙正,郁道银,陈晓冬等.基于光流法的冠状动脉造影图像序列中血管运动的估计.工程学学报,2003,(3):91-96
[46]李超,熊璋,赫阳等.基于帧间差的区域光流分析及其应用.计算机工程与应用. 2005,(31):199-201
[47]章毓晋.图像工程(第2版).北京,清华大学出版社.2007
[48]冯伟.视频序列运动目标检测与识别方法研究.[硕士学位论文].西安:西北工业大学,2003
[49]谭优,王泽勇.图像阈值分割算法实用技术研究与比较.微计算机信息,2007, 23(24):298-233
[50]Hsu C. -T., Hsieh S. -M. Region Tracking for Non-Rigid Video Objects in a Non-Parametric MAP Framework. Signal Processing:Image Communication. 2006, 21(3):235-251
[51]宋新,罗军,王鲁平等.基于GVF Snake的运动目标跟踪方法.红外与激光工程.2006.35(1):111-116
[52]MoravecHP. Towards automatic visual obstacle avoidance. In: Proceedings of International Joint conference on Artificial Intelligence, cambridge, MA, USA, 1977: 584-590
[53]Chris Harris, Mike Stephens. A combined Corner and Edge Detecto. Manchester Proceedings of the 4th A1vey Vision Conference, 1988: 147-151