基于MSPF的实时监控多目标跟踪算法研究

王相海; 方玲玲; 丛志环

doi:10.3724/SP.J.1004.2012.00139

基于MSPF的实时监控多目标跟踪算法研究

doi: 10.3724/SP.J.1004.2012.00139

1.
辽宁师范大学计算机与信息技术学院大连 116029;
2.
苏州大学江苏省计算机信息处理技术重点实验室苏州 215006

详细信息

通讯作者:
王相海博士,教授.主要研究方向为计算机图形学及本文通信作者.E-mail: xhwang@lnnu.edu.cn

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出版历程
- 收稿日期: 2009-12-07
- 修回日期: 2011-09-05
- 刊出日期: 2012-01-20

Research on Real-time Multi-targetbaselineskip Tracking Algorithm Based on MSPF

1.
College of Computer and Information Technology, Liaoning Normal University, Dalian 116029;
2.
Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006

摘要

摘要: 近年来,实时监控下多目标跟踪作为智能交通系统(Intelligent transportation system,ITS)的重要组成部分受到关注.传统多目标跟踪方法通常具有处理速度慢、容易对交叉行进车辆产生误匹配等问题.本文首先对基于贝叶斯规则的车辆视频复杂背景的建模及运动目标的检测进行研究,在此基础上提出一种基于Meanshift粒子滤波(Mean shift particle filter,MSPF)的多目标跟踪算法,首先对每一目标车辆在下一帧可能出现的范围进行预测,对单目标和多目标情况采用不同的检测策略,避免了全局搜索,提高了跟踪速度;通过构造基于最新观测信息的重要性密度函数,提高了MSPF算法在复杂背景情况下追踪部分遮挡及交叉车辆的准确性和鲁棒性.仿真实验结果验证了所提出算法的有效性.
- 视频车辆 /
- 多目标跟踪 /
- Mean shift 粒子滤波 /
- 鲁棒性
Abstract: Recently, real-time monitoring multi-target tracking as an important component of intelligent transportation system (ITS) has been paid much attention. The traditional multi-target tracking algorithm has problems that the processing speed is slow and the false matches may happen when vehicles cross. Firstly, the algorithm detects moving targets through modeling a complex background based on Bayesian rules, then introduces a multi-target tracking algorithm based on mean shift particle filter (MSPF). Firstly, the algorithm predicts the extent possible by the use of MSPF for each vehicle in the next frame, uses different detection strategies for simple or multiple targets to avoid a global search and improve the tracking speed; by constructing the importance density function based on the latest observations, the algorithm can achieve an accurate and robust tracking in the part of the block and cross-vehicle. Simulation results verify the proposed algorithm.
- Video vehicle /
- multi-target tracking /
- mean shift particle filter(MSPF) /
- robustness

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参考文献(1)

[1]

Yan Jie-Feng. Research on Vehicle Detection and Segmentation Methods in Video Based Traffic Surveillance [Ph.D. dissertation],University of Science and Technology of China,China ,2008(严捷丰. 交通视频监控中的车辆检测与分割方法研究 [博士学位论文],中国科学技术大学,中国,2008)[2] Wang Kun-Feng,Li Zhen-Jiang,Tang Shu-Ming. Visual traffic data collection approach based on multi-features fusion. Acta Automatica Sinica,2011,37(3):322-330(王坤峰,李镇江,汤淑明. 基于多特征融合的视频交通数据采集方法. 自动化学报,2011,37(3):322-330)[3] Wu Cong,Li Bo,Dong Rong,Chen Qi-Mei. Detecting traffic parameters based on vehicle clustering from video. Acta Automatica Sinica,2011,37(5):569-576(吴聪,李勃,董蓉,陈启美. 基于车型聚类的交通流参数视频检测. 自动化学报,2011,37(5):569-576)[4] Zhao Z X,Yu S Q,Wu X Y,Wang C L,Xu Y S. A multi-target tracking algorithm using texture for real-time surveillance. In:Proceedings of the IEEE International Conference on Robotics and Biomimetics. Bangkok,Thailand:IEEE,2009. 2150-2155[5] Comaniciu D,Ramesh V,Meer P. Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(5):564-577[6] Ryu H R,Huber M. A particle filter approach for multi-target tracking. In:Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. San Diego,USA:IEEE,2007. 2753-2760[7] Yonemoto S,Sato M. Multitarget tracking using mean-shift with particle filter based initialization. In:Proceedings of the 12th International Conference on Information Visualization. London,UK:IEEE,2008. 521-526[8] Zaboli S,Naderi S,Moghaddam A M E. Application of image mining for knowledge discovery of analyzed traffic images. In:Proceedings of the IEEE International Conference on Industrial Technology. Mumbai,India:IEEE,2006. 1066-1070[9] Gordon N J,Salmond D J,Smith A F M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings on Radar and Signal Processing,1993,140(2):107-113[10] Cheng Y. Mean shift mode seeking and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(8):790-799[11] Wang Xiang-Hai,Fang Ling-ling,Cong Zhi-Huan. Research on video vehicle tracking algorithm based on Kalman and particle filter. Journal of Image and Graphics,2010,15(11):1615-1622(王相海,方玲玲,丛志环. 卡尔曼粒子滤波的视频车辆跟踪算法研究. 中国图象图形学报,2010,15(11):1615-1622)[12] Li L Y,Huang W M,Gu I Y H,Tian Q. Foreground object detection from videos containing complex background. In:Proceedings of the 11th ACM International Conference on Multimedia. Berkeley,USA:ACM,2003. 2-10

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