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Online visual tracking based on subspace representation with continuous occlusion modeling

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Abstract

As an important issue in image processing and computer vision, online visual tracking acts a critical role in numerous lines of research and has many potential applications. This paper presents a novel tracking algorithm based on subspace representation with continuous occlusion handling, the contributions of which are threefolds. First, this paper develops an effective objective function to represent the tracked object, in which the object reconstruction, the sparsity of the error term and the spatial consistency of the error term are simultaneously considered. Then, we derive an iterative algorithm to solve the proposed objective function based on the accelerated proximal gradient framework, and therefore obtain the optimal representation coefficients and the possible occlusion conditions. Finally, based on the proposed representation model, we design an effective likelihood function and a simple model update scheme for building a robust tracker within the particle filter framework. We conduct many experiments to evaluate the proposed tracking algorithm in comparisons with other state-of-the-art trackers. Both qualitative and quantitative evaluations demonstrate the proposed tracker achieves good performance.

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Notes

  1. http://cvlab.hanyang.ac.kr/tracker_benchmark/benchmark_v10.html.

  2. The precision plot indicates the percentage of frames whose center location error (CLE) is within the given threshold, and the precision score is given by the score on a selected representative threshold (e.g., 20 pixels). In addition, the success plot demonstrates the ratio of successful frames whose overlap rate is larger than the given threshold, and the success score is evaluated by the area under curve (AUC).

  3. We note that the APGL1, MTT, IVT and our methods are all belong to the “linear representation”-based tracking methods.

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Acknowledgments

This work is supported by Natural Science Foundation of Liaoning Province (Grant No. 2013020018), General Science Research Projects of Liaoning Provincial Department of Education (Grant No. L2014536), Fundamental Research Funds for Central Universities (Grant No. DC201501010401, DC201501060201).

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Authors and Affiliations

  1. College of Electromechanical Engineering, Dalian Nationalities University, Dalian, 116600, China

    Chunjuan Bo, Changhong Liu & Qiang Yao

  2. College of Information and Communication Engineering, Dalian Nationalities University, Dalian, 116600, China

    Junxing Zhang

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  1. Chunjuan Bo
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  2. Junxing Zhang
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  3. Changhong Liu
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  4. Qiang Yao
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Correspondence to Junxing Zhang.

Additional information

Communicated by M. Cooper.

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Bo, C., Zhang, J., Liu, C. et al. Online visual tracking based on subspace representation with continuous occlusion modeling. Multimedia Systems 23, 357–368 (2017). https://doi.org/10.1007/s00530-015-0496-z

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