Computer Vision, IEEE International Conference on (2005)
Oct. 17, 2005 to Oct. 20, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICCV.2005.95
Changjiang Yang , University of Maryland at College Park
Ramani Duraiswami , University of Maryland at College Park
Larry Davis , University of Maryland at College Park
A very efficient and robust visual object tracking algorithm based on the particle filter is presented. The method characterizes the tracked objects using color and edge orientation histogram features. While the use of more features and samples can improve the robustness, the computational load required by the particle filter increases. To accelerate the algorithm while retaining robustness we adopt several enhancements in the algorithm. The first is the use of integral images  for efficiently computing the color features and edge orientation histograms, which allows a large amount of particles and a better description of the targets. Next, the observation likelihood based on multiple features is computed in a coarse-to-fine manner, which allows the computation to quickly focus on the more promising regions. Quasi-random sampling of the particles allows the filter to achieve a higher convergence rate. The resulting tracking algorithm maintains multiple hypotheses and offers robustness against clutter or short period occlusions. Experimental results demonstrate the efficiency and effectiveness of the algorithm for single and multiple object tracking.
L. Davis, C. Yang and R. Duraiswami, "Fast Multiple Object Tracking via a Hierarchical Particle Filter," Computer Vision, IEEE International Conference on(ICCV), Beijing, China, 2005, pp. 212-219.