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Object tracking based on Huber loss function

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Abstract

In this paper we present a novel visual tracking algorithm, in which object tracking is achieved by using subspace learning and Huber loss regularization in a particle filter framework. The changing appearance of tracked target is modeled by principle component analysis basis vectors and row group sparsity. This method takes advantage of the strengths of subspace representation and explicitly takes the underlying relationship between particle candidates into consideration in the tracker. The representation of each particle is learned via the multi-task sparse learning method. Huber loss function is employed to model the error between candidates and templates, yielding robust tracking. We utilize the alternating direction method of multipliers to solve the proposed representation model. In experiments we tested sixty representative video sequences that reflect the specific challenges of tracking and used both qualitative and quantitative metrics to evaluate the performance of our tracker. The experiment results demonstrated that the proposed tracking algorithm achieves superior performance compared to nine state-of-the-art tracking methods.

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

    Yong Wang

  2. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China

    Shiqiang Hu

  3. Departments of Radiology, Biomedical Informatics, Bioengineering, and Intelligent System (Computer Science), University of Pittsburgh, Pittsburgh, USA

    Shandong Wu

Authors
  1. Yong Wang
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  2. Shiqiang Hu
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  3. Shandong Wu
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Corresponding author

Correspondence to Shandong Wu.

Additional information

This work was jointly supported by the National Natural Science Foundation of China (No. 61374161) and China Aviation Science Foundation (No. 20142057006). This work was also partially supported by a National Institutes of Health (NIH)/National Cancer Institute (NCI) R01 Grant (#1R01CA193603).

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Wang, Y., Hu, S. & Wu, S. Object tracking based on Huber loss function. Vis Comput 35, 1641–1654 (2019). https://doi.org/10.1007/s00371-018-1563-1

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  • DOI: https://doi.org/10.1007/s00371-018-1563-1

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