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Visual tracking tracker via object proposals and co-trained kernelized correlation filters

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Abstract

Visual tracking is a challenging task in the field of computer vision with wide applications in intelligent and surveillance systems. Recently, correlation trackers have shown great achievement in visual tracking due to its high efficiency. However, such trackers have a problem of handling fast motion, motion blur, illumination variations, background clutter and drifting away caused by occlusion and thus may result in tracking failure. To solve this problem, we propose a tracker that is based on the object proposals and co-kernelized correlation filters (Co-KCF). The proposed tracker utilizes both object proposals and global prediction estimated by kernelized correlation filter scheme to obtain best proposals as prior information using spatial weight strategy in order to improve tracking performance of fast motion and motion blur. Since single kernel may lead to background clutter and drifting problem, Co-KCF has been employed to combat this defect and predict a new state of a target object. Extensive experiments demonstrate that our proposed tracker outperforms other existing state-of-the-art trackers.

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Acknowledgements

This work was supported by the University of Dar es Salaam, the National Natural Science Foundation of China (Grant No. 61175096, the NSFC No. 61300082), and the Liaoning BaiQianWan Talents program, Dalian Youth Scholar Foundation (No. 2017RQ151).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Dar es salaam, P.O. Box 33335, Dar es salaam, Tanzania

    Jimmy T. Mbelwa

  2. Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Haidian District, Beijing, 100081, China

    Jimmy T. Mbelwa & Qingjie Zhao

  3. School of Information and Communication Engineering, Dalian Minzu University, Jinzhou New District, Dalian, 116600, China

    Fasheng Wang

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  1. Jimmy T. Mbelwa
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  2. Qingjie Zhao
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  3. Fasheng Wang
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Correspondence to Jimmy T. Mbelwa.

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Mbelwa, J.T., Zhao, Q. & Wang, F. Visual tracking tracker via object proposals and co-trained kernelized correlation filters. Vis Comput 36, 1173–1187 (2020). https://doi.org/10.1007/s00371-019-01727-1

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