Tracking Objects Across Cameras by Incrementally Learning Inter-camera Colour Calibration and Patterns of Activity

This paper presents a scalable solution to the problem of tracking objects across spatially separated, uncalibrated, non-overlapping cameras. Unlike other approaches this technique uses an incremental learning method, to model both the colour variations and posterior probability distributions of spatio-temporal links between cameras. These operate in parallel and are then used with an appearance model of the object to track across spatially separated cameras. The approach requires no pre-calibration or batch preprocessing, is completely unsupervised, and becomes more accurate over time as evidence is accumulated.

[1]  R. Bowden,et al.  Towards automated wide area visual surveillance: tracking objects between spatially-separated, uncalibrated views , 2005 .

[2]  Jake K. Aggarwal,et al.  Tracking human motion using multiple cameras , 1996, Proceedings of 13th International Conference on Pattern Recognition.

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

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

[5]  Greg Welch,et al.  Ensuring color consistency across multiple cameras , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

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

[7]  Richard Bowden,et al.  A real time adaptive visual surveillance system for tracking low-resolution colour targets in dynamically changing scenes , 2003, Image Vis. Comput..

[8]  Shaogang Gong,et al.  Bayesian Modality Fusion for Tracking Multiple People with a Multi-Camera System , 2002 .

[9]  Ramesh C. Jain,et al.  An architecture for multiple perspective interactive video , 1995, MULTIMEDIA '95.

[10]  Michael J. Brooks,et al.  A Stochastic Approach to Tracking Objects Across Multiple Cameras , 2004, Australian Conference on Artificial Intelligence.

[11]  Fatih Murat Porikli,et al.  Inter-camera color calibration by correlation model function , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[12]  D. Makris.  Learning a Multi-camera Topology , 2003 .

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

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

[15]  Richard Bowden,et al.  Tracking objects across uncalibrated, arbitrary topology camera networks , 2006 .

[16]  T. Ellis,et al.  Wide area surveillance with a multi camera network , 2004 .

[17]  Julia Sturges,et al.  Locating basic colours in the munsell space , 1995 .