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A correlative classifiers approach based on particle filter and sample set for tracking occluded target

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Abstract

Target tracking is one of the most important issues in computer vision and has been applied in many fields of science, engineering and industry. Because of the occlusion during tracking, typical approaches with single classifier learn much of occluding background information which results in the decrease of tracking performance, and eventually lead to the failure of the tracking algorithm. This paper presents a new correlative classifiers approach to address the above problem. Our idea is to derive a group of correlative classifiers based on sample set method. Then we propose strategy to establish the classifiers and to query the suitable classifiers for the next frame tracking. In order to deal with nonlinear problem, particle filter is adopted and integrated with sample set method. For choosing the target from candidate particles, we define a similarity measurement between particles and sample set. The proposed sample set method includes the following steps. First, we cropped positive samples set around the target and negative samples set far away from the target. Second, we extracted average Haar-like feature from these samples and calculate their statistical characteristic which represents the target model. Third, we define the similarity measurement based on the statistical characteristic of these two sets to judge the similarity between candidate particles and target model. Finally, we choose the largest similarity score particle as the target in the new frame. A number of experiments show the robustness and efficiency of the proposed approach when compared with other state-of-the-art trackers.

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, 430072, China

    Kang Li, Fa-zhi He, Hai-ping Yu & Xiao Chen

  2. School of Computer Science and Information Engineering, Hubei University, Wuhan, 430062, China

    Kang Li

  3. The State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072, China

    Fa-zhi He, Hai-ping Yu & Xiao Chen

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  1. Kang Li
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  2. Fa-zhi He
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  3. Hai-ping Yu
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Correspondence to Fa-zhi He.

Additional information

This paper is supported by the National Science Foundation of China (61472289), National Key Research and Development Project (2016YFC0106305), and The Key Technology R&D Program of Hubei Provence (2014BAA153).

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Li, K., He, Fz., Yu, Hp. et al. A correlative classifiers approach based on particle filter and sample set for tracking occluded target. Appl. Math. J. Chin. Univ. 32, 294–312 (2017). https://doi.org/10.1007/s11766-017-3466-8

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  • DOI: https://doi.org/10.1007/s11766-017-3466-8

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