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IMM/MHT fusing feature information in visual tracking

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Journal of Electronics (China)

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Abstract

In multi-target tracking, Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However, traditional MHT can not make full use of motion information. In this work, we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.

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Authors and Affiliations

  1. School of Information Science and Engineering, Graduate University of Chinese Academy of Sciences, Beijing, 100190, China

    Shuangquan Li, Shuyan Sun, Sheng Jiang, Zhipei Huang & Jiankang Wu

  2. Room 504, Moshi Building, Institute of Automation, Chinese Academy of Sciences., No. 95 Zhongguancun East Road, Haidian District, Beijing, 100190, China

    Shuangquan Li

Authors
  1. Shuangquan Li
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  2. Shuyan Sun
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  3. Sheng Jiang
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  4. Zhipei Huang
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  5. Jiankang Wu
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Corresponding author

Correspondence to Shuangquan Li.

Additional information

Supported by the National Natural Science Foundation of China (No. 60772154) and by the President Foundation of Graduate University of Chinese Academy of Sciences (No. 085102GN00).

Li Shuangquan, born in 1984, Male, Master student.

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Li, S., Sun, S., Jiang, S. et al. IMM/MHT fusing feature information in visual tracking. J. Electron.(China) 26, 765–770 (2009). https://doi.org/10.1007/s11767-010-0287-9

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  • DOI: https://doi.org/10.1007/s11767-010-0287-9

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