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An improved target tracking algorithm based on spatio-temporal context under occlusions

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Abstract

Target tracking is a popular but challenging problem in computer vision field. Due to many aggravating factors such as position transformation, illumination, occlusion, it is difficult to achieve robust target tracking. According to the above constraints, an improved target tracking algorithm based on spatio-temporal context (STC) under occlusions is proposed. On the basis of STC, the proposed method introduces a novel mechanism for dealing with occlusion, the scale update, and the learning rate update to reduce the error update of the model and restrain error accumulation. As a consequence, the tracking performance can be improved efficiently. Extensive experimental results show that our algorithm outperforms the original STC algorithm and some other state-of-the-art algorithms.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (61573182).

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Authors and Affiliations

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, Jiangsu, China

    Xin Yang, Dake Zhou & Yifan Zhang

  2. Jiangsu College of Engineering and Technology, Nantong, 226000, China

    Songyan Zhu

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  1. Xin Yang
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  2. Songyan Zhu
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  3. Dake Zhou
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  4. Yifan Zhang
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Correspondence to Xin Yang.

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Yang, X., Zhu, S., Zhou, D. et al. An improved target tracking algorithm based on spatio-temporal context under occlusions. Multidim Syst Sign Process 31, 329–344 (2020). https://doi.org/10.1007/s11045-019-00664-5

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  • DOI: https://doi.org/10.1007/s11045-019-00664-5

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