A statistical method for object localization in multi-camera tracking

The aim of the paper is to show that localization of observed moving objects is possible in a multicamera environment, based on the simple foreground mask and the estimation of the size distribution on image plane. The method is flexible, it can handle arbitrary number of cameras and objects. It is based only on change detection masks and does not depend on appearance information. Due to its statistical nature the proposed method efficiently handles such challenging situations as changes in viewpoint, occlusions due to view variations, background clutter.

[1]  Shaogang Gong,et al.  Tracking multiple people with a multi-camera system , 2001, Proceedings 2001 IEEE Workshop on Multi-Object Tracking.

[2]  Mei Han,et al.  An algorithm for multiple object trajectory tracking , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[3]  L. Havasi,et al.  Extraction of horizontal vanishing line using shapes and statistical error propagation , 2006 .

[4]  Aaron F. Bobick,et al.  Fast Lighting Independent Background Subtraction , 2004, International Journal of Computer Vision.

[5]  M. Shah,et al.  Object tracking: A survey , 2006, CSUR.

[6]  Leonidas J. Guibas,et al.  Counting people in crowds with a real-time network of simple image sensors , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[7]  Mubarak Shah,et al.  A Multiview Approach to Tracking People in Crowded Scenes Using a Planar Homography Constraint , 2006, ECCV.

[8]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Paolo Remagnino,et al.  A multi-agent framework for visual surveillance , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[10]  H. Saito,et al.  Parallel tracking of all soccer players by integrating detected positions in multiple view images , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[11]  A. M. Tekalp,et al.  Multiple camera fusion for multi-object tracking , 2001, Proceedings 2001 IEEE Workshop on Multi-Object Tracking.

[12]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[13]  J. Krumm,et al.  Multi-camera multi-person tracking for EasyLiving , 2000, Proceedings Third IEEE International Workshop on Visual Surveillance.

[14]  Larry S. Davis,et al.  M2Tracker: A Multi-view Approach to Segmenting and Tracking People in a Cluttered Scene Using Region-Based Stereo , 2002, ECCV.

[15]  Mubarak Shah,et al.  Tracking Multiple Occluding People by Localizing on Multiple Scene Planes , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.