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Occlusion Detection via Structured Sparse Learning for Robust Object Tracking

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Computer Vision in Sports

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios, these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object’s track. This is the case when significant occlusion occurs. To accommodate for nonsparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Extensive experimental results show that our proposed tracker consistently outperforms the state-of-the-art trackers.

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Acknowledgments

This study is supported by the research grant for the Human Sixth Sense Programme at the Advanced Digital Sciences Center from Singapore’s Agency for Science, Technology, and Research (A\(^*\)STAR).

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Authors and Affiliations

  1. Advanced Digital Sciences Center of Illinois, Singapore, Singapore

    Tianzhu Zhang

  2. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Bernard Ghanem

  3. Institute of Automation, Chinese Academy of Sciences, CSIDM, People’s Republic of China

    Changsheng Xu

  4. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Narendra Ahuja

Authors
  1. Tianzhu Zhang
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  2. Bernard Ghanem
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  3. Changsheng Xu
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  4. Narendra Ahuja
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Corresponding author

Correspondence to Tianzhu Zhang .

Editor information

Editors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Thomas B. Moeslund

  2. BBC R&D, London, United Kingdom

    Graham Thomas

  3. University of Surrey, Guildford, Surrey, United Kingdom

    Adrian Hilton

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Zhang, T., Ghanem, B., Xu, C., Ahuja, N. (2014). Occlusion Detection via Structured Sparse Learning for Robust Object Tracking. In: Moeslund, T., Thomas, G., Hilton, A. (eds) Computer Vision in Sports. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-09396-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-09396-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09395-6

  • Online ISBN: 978-3-319-09396-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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