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Particle filter re-detection for visual tracking via correlation filters

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Abstract

Most of the correlation filter based tracking algorithms can achieve good performance and maintain fast computational speed. However, in some complicated tracking scenes, there is a fatal defect that causes the object to be located inaccurately, which is the trackers excessively dependent on the maximum response value to determine the object location. In order to address this problem, we propose a particle filter redetection based tracking approach for accurate object localization. During the tracking process, the kernelized correlation filter (KCF) based tracker can locate the object by relying on the maximum response value of the response map; when the response map becomes ambiguous, the tracking result becomes unreliable correspondingly. Our redetection model can provide abundant object candidates by particle resampling strategy to detect the object accordingly. Additionally, for the target scale variation problem, we give a new object scale evaluation mechanism, which merely considers the differences between the maximum response values in consecutive frames to determine the scale change of the object target. Extensive experiments on OTB2013 and OTB2015 datasets demonstrate that the proposed tracker performs favorably in relation to the state-of-the-art methods.

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Acknowledgment

This study was supported by by the National Natural Science Foundation of China (Grant No. 61672183), the Shenzhen Research Council (Grant No.JCYJ20170413104556946, JCYJ20170815113552036, JCYJ20160226201453085), by Science and Technology Planning Project of Guanddong Province (Grant No. 2016B090918047), and by Shenzhen Medical Biometrics Perception and Analysis Engineering Laboratory.

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    1. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China

      Di Yuan, Xiaohuan Lu, Donghao Li & Yingyi Liang

    2. School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, China

      Xinming Zhang

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    D. Yuan and X. Lu are contributed equally to this work and should be considered co-first authors.

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    Yuan, D., Lu, X., Li, D. et al. Particle filter re-detection for visual tracking via correlation filters. Multimed Tools Appl 78, 14277–14301 (2019). https://doi.org/10.1007/s11042-018-6800-0

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