Multi-camera People Tracking by Collaborative Particle Filters and Principal Axis-Based Integration
This paper presents a novel approach to tracking people in multiple cameras. A target is tracked not only in each camera but also in the ground plane by individual particle filters. These particle filters collaborate in two different ways. First, the particle filters in each camera pass messages to those in the ground plane where the multi-camera information is integrated by intersecting the targets’ principal axes. This largely relaxes the dependence on precise foot positions when mapping targets from images to the ground plane using homographies. Secondly, the fusion results in the ground plane are then incorporated by each camera as boosted proposal functions. A mixture proposal function is composed for each tracker in a camera by combining an independent transition kernel and the boosted proposal function. Experiments show that our approach achieves more reliable results using less computational resources than conventional methods.
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- 1.Du, W., Piater, J.: Multi-view object tracking using sequential belief propagation. In: Asian Conference on Computer Vision, Hyderabad, India (2006)Google Scholar
- 2.Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multi-camera people tracking with a probabilistic occupancy map. IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)Google Scholar
- 3.Hayet, J.-B., Piater, J., Verly, J.: Robust incremental rectification of sports video sequences. In: British Machine Vision Conference, Kingston, UK, pp. 687–696 (2004)Google Scholar
- 4.Hu, W.-M., Hu, M., Zhou, X., Tan, T.-N., Lou, J., Maybank, S.J.: Principal axis-based correspondence between multiple cameras for people tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(4), 663–671 (2006)Google Scholar
- 5.Khan, S.M., Shah, M.: A multiview approach to tracking people in crowded scenes using a planar homography constraint. In: ECCV, pp. 98–109 (2006)Google Scholar
- 6.Kim, K., Davis, L.S.: Multi-camera tracking and segmentation of occluded people on ground plane using search-guided particle filtering. In: ECCV, pp. 98–109 (2006)Google Scholar
- 7.Kobayashi, Y., Sugimura, D., Sato, Y.: 3d head tracking using the particle filter with cascaded classifiers. In: BMVC (2006)Google Scholar
- 9.Nummiaro, K., Koller-Meier, E., Svoboda, T., Roth, D., van Gool, L.: Color-based object tracking in multi-camera environment. In: Michaelis, B., Krell, G. (eds.) Pattern Recognition. LNCS, vol. 2781, Springer, Heidelberg (2003)Google Scholar
- 11.Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: European Conference on Computer Vision, Copenhagen, Denmark, vol. 1, pp. 661–675 (2002)Google Scholar