Video object tracking using improved chamfer matching and condensation particle filter

Tao Wu; Xiaoqing Ding; Shengjin Wang; Kongqiao Wang

doi:10.1117/12.766388

26 February 2008 Video object tracking using improved chamfer matching and condensation particle filter

Tao Wu, Xiaoqing Ding, Shengjin Wang, Kongqiao Wang

Proceedings Volume 6813, Image Processing: Machine Vision Applications; 681304 (2008) https://doi.org/10.1117/12.766388
Event: Electronic Imaging, 2008, San Jose, California, United States

Abstract

Object tracking is an essential problem in the field of video and image processing. Although tracking algorithms working on gray video are convenient in actual applications, they are more difficult to be developed than those using color features, since less information is taken into account. Few researches have been dedicated to tracking object using edge information. In this paper, we proposed a novel video tracking algorithm based on edge information for gray videos. This method adopts the combination of a condensation particle filter and an improved chamfer matching. The improved chamfer matching is rotation invariant and capable of estimating the shift between an observed image patch and a template by an orientation distance transform. A modified discriminative likelihood measurement method that focuses on the difference is adopted. These values are normalized and used as the weights of particles which predict and track the object. Experiment results show that our modifications to chamfer matching improve its performance in video tracking problem. And the algorithm is stable, robust, and can effectively handle rotation distortion. Further work can be done on updating the template to adapt to significant viewpoint and scale changes of the appearance of the object during the tracking process.

Citation Download Citation

Tao Wu, Xiaoqing Ding, Shengjin Wang, and Kongqiao Wang "Video object tracking using improved chamfer matching and condensation particle filter", Proc. SPIE 6813, Image Processing: Machine Vision Applications, 681304 (26 February 2008); https://doi.org/10.1117/12.766388

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