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Real-time visual tracking based on improved perceptual hashing

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Abstract

Video object tracking represents a very important computer vision domain. In this paper, a perceptual hashing based template-matching method for object tracking is proposed to efficiently track objects in challenging video sequences. In the tracking process, we first apply three existing basic perceptual hashing techniques to visual tracking, namely average hash (aHash), perceptive hash (pHash) and difference hash (dHash). Compared with previous tracking methods such as mean-shift or compressive tracking (CT), perceptual hashing-based tracking outperforms in terms of efficiency and accuracy. In order to further improve the accuracy of object localization and the robustness of tracking, we propose Laplace-based Hash (LHash) and Laplace-based Difference Hash (LDHash). By qualitative and quantitative comparison with some representative tracking algorithms, experimental results show that our improved perceptual hashing-based tracking algorithms perform favorably against the state-of-the-art algorithms under various challenging environments in terms of time cost, accuracy and robustness. Since our improved perceptual hashing can be a compact and efficient representation of objects, it can be further applied to fusing with depth information for more robust RGB-D video tracking.

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Acknowledgments

This work is supported by National Natural Science Foundation of China (61463032, 61563035, and 81501560) and Scientific Research Foundation for Returned Scholars, Ministry of Education of China.

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Authors and Affiliations

  1. Institute of Cyber-Systems and control, Zhejiang University, HangZhou, 310027, China

    Mengjuan Fei

  2. Intelligent Systems and Biomedical Robotics Group, School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, UK

    Zhaojie Ju

  3. The University of Western Ontario, London, ON, Canada

    Xiantong Zhen

  4. Laboratory of Ubiquitous Vision Perception and Intelligent Computing, School of Information Engineering, Nanchang University, Nanchang, 330031, China

    Jing Li

Authors
  1. Mengjuan Fei
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  2. Zhaojie Ju
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  3. Xiantong Zhen
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  4. Jing Li
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Correspondence to Jing Li.

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Fei, M., Ju, Z., Zhen, X. et al. Real-time visual tracking based on improved perceptual hashing. Multimed Tools Appl 76, 4617–4634 (2017). https://doi.org/10.1007/s11042-016-3723-5

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  • DOI: https://doi.org/10.1007/s11042-016-3723-5

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