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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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Correspondence to Shasha Zhao.

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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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Keywords:

  • feature selection
  • video monitoring
  • target tracking
  • moving background
  • image segmentation