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A new TLD target tracking method based on improved correlation filter and adaptive scale

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Abstract

Target tracking is a popular but challenging problem in computer vision field. Due to many disturbing factors such as position transformation, illumination, and occlusion, it is difficult to achieve continuous target tracking. On the basis of the above analyses, a novel target tracking method based on correlation filters is proposed in this paper. This method uses the improved Tracking–Learning–Detection (TLD) tracking framework which combines the tracker with the detector through the learning mechanism. In the TLD tracking framework, the Spatially Regularized Discriminatively Correlation Filters tracker is used and improved. In addition, the adaptive tracking scale is realized according to the confidence of the searching area. The experimental results show that the proposed algorithm can effectively deal with the attitude change and the illumination problem so that it has better robustness and stability for target continuous tracking.

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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, Sijun Xia & Dake Zhou

  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. Sijun Xia
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  4. Dake Zhou
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Correspondence to Xin Yang.

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Yang, X., Zhu, S., Xia, S. et al. A new TLD target tracking method based on improved correlation filter and adaptive scale. Vis Comput 36, 1783–1795 (2020). https://doi.org/10.1007/s00371-019-01772-w

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