WNAG Wei GUO Yu YU Xin. Moving object tracking with related multi-regions based on Kalman filter[J]. Journal of Computer Applications, 2012, 32(11): 3174-3177.
[1]
ALPER Y, OMAR J, MUBARAK S. Object tracking:A survey[J]. ACM Journal of Computing Surveys, 2006, 38(4):1-45.
[2]
COMANICIU D, REMESH V, MEER P. Real-time tracking of non-rigid objects using Mean Shift[C]// Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2000, 2:142-149.
[3]
COMANICIU D, REMESH V, MEER P. Kernel-based object tracking[J]. IEEE Transactions Pattern Analysis and Machine Intelligence, 2003, 25(5):564-575.
[4]
GENNARI G, HAGER G D. Probabilistic data association methods in visual tracking of groups[C]// Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2004: 876-881.
[5]
高书秀,黄剑华,唐降龙,等. 基于多区域的人体运动跟踪研究与应用[J]. 微计算机信息,2008,24(7):268-270.
[6]
王玉茹,刘家峰,刘国军,等. 基于多区域联合粒子滤波的人体运动跟踪[J]. 自动化学报,2009,35(11):1387-1393.
[7]
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[J]. 中国图象图形学报,2010,15(11):1615-1622.
[8]
周尚波,胡鹏,柳玉炯. 基于改进Mean-Shift与自适应Kalman滤波的视频目标跟踪[J]. joca,2010,30(6):1573-1576.
[9]
GUI JUN, ZHANG TAO, LIU LIJUN. The application of Kalman filtering in the system of image difference moving objects tracking[C]// 2011 International Conference on Computational and Information Sciences. Washington, DC: IEEE Computer Society, 2011:83-86.
[10]
李培华. 序列图像中运动目标跟踪方法[M]. 北京:科学出版社,2010.
[11]
赵春晖,潘泉,梁彦,等. 视频图像运动目标分析[M]. 北京:国防工业出版社,2011.
[12]
CAVIAR test case scenarios [EB/OL]. [2012-02-20]. http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATA1.