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Target Tracking Based on Multi Feature Selection Fusion Compensation in Monitoring Video

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Abstract

This thesis is mainly targeted at self-adaptation adjustment in the search region: at first, design a staging predation space self-adaptation scale strategy bat algorithm (AP-RBA), and then, use AP-RBA algorithm to establish a target tracking strategy of optimized particle filter which can effectively solve two kinds of problems: (1) particle impoverishment phenomena produced in particle filter; (2) effective tracking targets based on few particles, thus simplifying complexity of particle filter, and then, adopt the criterion weight strategy to achieve maximum a posteriori and change of criterion weight to realize effective improvement of particle distribution and promote efficiency of particle filter process.

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Funding

This work is supported by Anhui province outstanding young talents support program (gxyq2017157, gxyq2017159); Anhui province major teaching reform project (2016jyxm0777); Anhui natural science research project (KJ2017A838, KJ2017A837, KJ2018A0669, KJ2018A0670).

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Authors and Affiliations

  1. College of Information Engineering, Fuyang Normal University, 236041, Fuyang, China

    Yingying Feng, Shasha Zhao & Hui Liu

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  1. Yingying Feng
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  2. Shasha Zhao
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  3. Hui Liu
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Corresponding author

Correspondence to Shasha Zhao.

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The authors declare that they have no conflicts of interest.

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Yingying Feng, Zhao, S. & Liu, H. Target Tracking Based on Multi Feature Selection Fusion Compensation in Monitoring Video. Aut. Control Comp. Sci. 53, 522–531 (2019). https://doi.org/10.3103/S0146411619060051

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  • DOI: https://doi.org/10.3103/S0146411619060051

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