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A real-time object tracking via L2-RLS and compressed Haar-like features matching

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Abstract

In this paper, we present a robust and fast online object tracking algorithm, in which object tracking is achieved by combining L2-regularized least square (L2-RLS) and compressed Haar-like features matching in a Bayesian inference framework. Firstly, the extent of occlusion can be evaluated by L2 tracker. Secondly, the compressed features matching method is used to locate the target object if the extent of occlusion satisfies two inequality constraints. Finally, most of the insignificant samples are removed before computing the compressed features, which makes the computational load of our fused algorithm be only slightly higher than L2 tracker. Both qualitative and quantitative evaluations on numerous challenging image sequences demonstrate that the proposed method is more robust and stable than L2 tracker when the target object undergoes pose variation or rotation, and a paired T-test verifies that it significantly outperforms other state-of-the-art algorithms in terms of accuracy. In addition, our tracker meets the requirement of real-time tracking.

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Acknowledgments

This work was supported by National Science Foundation of China(NSFC) under Grand 51479159 and Soft Science Project of China’s Ministry of Transport under Grand 2013-322-811-470.

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Authors and Affiliations

  1. Key Laboratory of Fiber Optic Sensing Technology and Information Processing Ministry of Education, Wuhan University of Technology, Wuhan, 430070, China

    Zhengping Wu, Jie Yang, Haibo Liu & Qingnian Zhang

  2. China Three Gorges University, Yichang, 443002, China

    Zhengping Wu

Authors
  1. Zhengping Wu
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  2. Jie Yang
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  3. Haibo Liu
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  4. Qingnian Zhang
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Correspondence to Zhengping Wu.

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Wu, Z., Yang, J., Liu, H. et al. A real-time object tracking via L2-RLS and compressed Haar-like features matching. Multimed Tools Appl 75, 9427–9443 (2016). https://doi.org/10.1007/s11042-016-3356-8

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  • DOI: https://doi.org/10.1007/s11042-016-3356-8

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