KNIGHT M : A REAL TIME SURVEILLANCE SYSTEM FOR MULTIPLE OVERLAPPING AND NON-OVERLAPPING CAMERAS

In this paper, we present a wide area surveillance system that detects, tracks and classifies moving objects across multiple cameras. At the single camera level, tracking is performed using a voting based approach that utilizes color and shape cues to establish correspondence. The system uses the single camera tracking results along with the relationship between camera field of view (FOV) boundaries to establish correspondence between views of the same object in multiple cameras. To this end, a novel approach is described to find the relationships between the FOV lines of cameras. The proposed approach combines tracking in cameras with overlapping and/or non-overlapping FOVs in a unified framework, without requiring explicit calibration. The proposed algorithm has been implemented in a real time system. The system uses a client-server architecture and runs at 10 Hz with three cameras.

[1]  Stuart J. Russell,et al.  Object identification in a Bayesian context , 1997, IJCAI 1997.

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

[3]  Lily Lee,et al.  Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Mubarak Shah,et al.  Human tracking in multiple cameras , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[5]  Takeo Kanade,et al.  Algorithms for cooperative multisensor surveillance , 2001, Proc. IEEE.

[6]  Mubarak Shah,et al.  A hierarchical approach to robust background subtraction using color and gradient information , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[7]  Mubarak Shah,et al.  Tracking and Object Classification for Automated Surveillance , 2002, ECCV.