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Fast compressive tracking combined with Kalman filter

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Abstract

Compressive tracking refers to a group of high-speed algorithms for real-time object tracking. Many tracking algorithms may not generate accurate tracking results because they used fixed learning rates, and sometime lose targets when objects are occluded or deformed. To address these problems, a fast tracking algorithm combined with Kalman filter was proposed in this research. Firstly, an object location was initialized by the predicted value of Kalman filter when it was occluded, and the Kalman update was implemented only when the object was detected. The object location obtained in the Kalman update stage was used later as the initial position in the next frame. Secondly, when the distribution of positive samples satisfied a threshold, an adaptive learning rate was then updated. Finally, the naive Bayes classifier was updated with samples which had more different features. In the experiment, the proposed algorithm was compared with other state-of-the-art algorithms on seven publicly tested sequences, demonstrating that it had higher tracking accuracy and robustness in conditions such as occlusion, deformation and rotation.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (61601358), the Natural Science Basic Research Plan in Shaanxi Province of China (2016JM6030), the Scientific Research Program funded by Shaanxi Provincial Education Department (18JK0349).

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Authors and Affiliations

  1. School of Computer Science, Xi’an Polytechnic University, Xi’an, 710048, China

    Jinguang Chen, Xiaoxing Li, Mingming Wang, Lili Ma & Bugao Xu

  2. Department of Computer Science and Engineering, University of North Texas, Denton, TX, 76203, USA

    Bugao Xu

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  1. Jinguang Chen
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  2. Xiaoxing Li
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Correspondence to Bugao Xu.

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Chen, J., Li, X., Wang, M. et al. Fast compressive tracking combined with Kalman filter. Multimed Tools Appl 78, 22463–22477 (2019). https://doi.org/10.1007/s11042-019-7514-7

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