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A refined particle filter based on determined level set model for robust contour tracking

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Abstract

Traditional particle filter which uses simple geometric shapes for representation cannot track objects with complex shape accurately. In this paper, we propose a refined particle filter method for contour tracking based on a determined binary level set model (DBLSM). In contrast with other previous work, the computational efficiency is greatly improved due to the simple form of the level set function. The DBLSM adds prior knowledge of the target model to the implementation of curve evolution which improves the curve acting principle and ensures a more accurate convergence to the target. Finally, we perform curve evolution in the update step of particle filter to make good use of the observation at current time. Some appearance information are considered together with the energy function to measure weights for particles, which can identify the target more accurately. Experiment results on several challenging video sequences have verified the proposed algorithm is efficient and effective in many complicated scenes.

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Acknowledgments

The work was supported by the National Natural Science Foundation of China (Grant No. 61071180) and Key Program (Grant No.61133003).

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Authors and Affiliations

  1. Harbin Institute of Technology, 92 West Dazhi Street, Harbin, 150001, China

    Xin Sun & Hongxun Yao

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  1. Xin Sun
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  2. Hongxun Yao
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Correspondence to Hongxun Yao.

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Sun, X., Yao, H. A refined particle filter based on determined level set model for robust contour tracking. Machine Vision and Applications 25, 1727–1736 (2014). https://doi.org/10.1007/s00138-013-0553-6

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