Bayesian-Competitive Consistent Labeling for People Surveillance

This paper presents a novel and robust approach to consistent labeling for people surveillance in multicamera systems. A general framework scalable to any number of cameras with overlapped views is devised. An offline training process automatically computes ground-plane homography and recovers epipolar geometry. When a new object is detected in any one camera, hypotheses for potential matching objects in the other cameras are established. Each of the hypotheses is evaluated using a prior and likelihood value. The prior accounts for the positions of the potential matching objects, while the likelihood is computed by warping the vertical axis of the new object on the field of view of the other cameras and measuring the amount of match. In the likelihood, two contributions (forward and backward) are considered so as to correctly handle the case of groups of people merged into single objects. Eventually, a maximum-a-posteriori approach estimates the best label assignment for the new object. Comparisons with other methods based on homography and extensive outdoor experiments demonstrate that the proposed approach is accurate and robust in coping with segmentation errors and in disambiguating groups.

[1]  Surendra Ranganath,et al.  Multi-camera people tracking using Bayesian networks , 2003, Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint.

[2]  L. Davis,et al.  M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene , 2003, International Journal of Computer Vision.

[3]  Luc Van Gool,et al.  Color-Based Object Tracking in Multi-camera Environments , 2003, DAGM-Symposium.

[4]  Rita Cucchiara,et al.  Detecting Moving Objects, Ghosts, and Shadows in Video Streams , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Mubarak Shah,et al.  Consistent Labeling of Tracked Objects in Multiple Cameras with Overlapping Fields of View , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  O. Faugeras,et al.  On determining the fundamental matrix : analysis of different methods and experimental results , 1993 .

[7]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[8]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

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

[10]  Paolo Remagnino,et al.  Multi-camera colour tracking , 1999, Proceedings Second IEEE Workshop on Visual Surveillance (VS'99) (Cat. No.98-89223).

[11]  Christian Bräuer-Burchardt,et al.  Robust vanishing point determination in noisy images , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

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

[13]  A. M. Tekalp,et al.  Multiple camera tracking of interacting and occluded human motion , 2001, Proc. IEEE.

[14]  Gérard G. Medioni,et al.  Object reacquisition using invariant appearance model , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[15]  Simone Calderara,et al.  Entry edge of field of view for multi-camera tracking in distributed video surveillance , 2005, IEEE Conference on Advanced Video and Signal Based Surveillance, 2005..

[16]  Ramin Zabih,et al.  Bayesian multi-camera surveillance , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[17]  Yaacov Ritov,et al.  Tracking Many Objects with Many Sensors , 1999, IJCAI.

[18]  Wei Niu,et al.  Human activity detection and recognition for video surveillance , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

[19]  Rita Cucchiara,et al.  Probabilistic people tracking for occlusion handling , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[20]  Gérard G. Medioni,et al.  Continuous tracking within and across camera streams , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[21]  Ramakant Nevatia,et al.  Self-calibration of a camera from video of a walking human , 2002, Object recognition supported by user interaction for service robots.

[22]  Gideon P. Stein,et al.  Tracking from multiple view points: Self-calibration of space and time , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[23]  Jiang Li,et al.  Color based multiple people tracking , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

[24]  Chris Stauffer,et al.  Automated multi-camera planar tracking correspondence modeling , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[25]  Jake K. Aggarwal,et al.  Object tracking in an outdoor environment using fusion of features and cameras , 2006, Image Vis. Comput..

[26]  Mubarak Shah,et al.  Tracking across multiple cameras with disjoint views , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[27]  Stuart J. Russell,et al.  Object Identification in a Bayesian Context , 1997, IJCAI.

[28]  Rama Chellappa,et al.  Robust two-camera tracking using homography , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.