Fixed Point Probability Field for Occlusion Handling

In this paper, we show that in a multi-camera context, we can effectively handle occlusions at each time frame independently, even when the only available data comes from the binary output of a fairly primitive motion detector. We start from occupancy probability estimates in a top view and rely on a generative model to yield probability images to be compared with the actual input images. We then refine the estimates so that the probability images match the binary input images as well as possible. We demonstrate the quality of our results on several sequences involving complex occlusions.

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

[2]  K. Otsuka,et al.  Multiview occlusion analysis for tracking densely populated objects based on 2-D visual angles , 2004, CVPR 2004.

[3]  R. Cipolla,et al.  A probabilistic framework for space carving , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[4]  Ramakant Nevatia,et al.  Tracking multiple humans in crowded environment , 2004, CVPR 2004.

[5]  James J. Little,et al.  A Boosted Particle Filter: Multitarget Detection and Tracking , 2004, ECCV.

[6]  Jake K. Aggarwal,et al.  Automatic tracking of human motion in indoor scenes across multiple synchronized video streams , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[7]  James Black,et al.  Multi view image surveillance and tracking , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

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

[9]  Larry S. Davis,et al.  W/sup 4/: Who? When? Where? What? A real time system for detecting and tracking people , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[10]  Dariu Gavrila,et al.  A Bayesian Framework for Multi-cue 3D Object Tracking , 2004, ECCV.