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Enhanced Laplacian Group Sparse Learning with Lifespan Outlier Rejection for Visual Tracking

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9007))

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Abstract

Recently, sparse based learning methods have attracted much attention in robust visual tracking due to their effectiveness and promising tracking results. By representing the target object sparsely, utilising only a few adaptive dictionary templates, in this paper, we introduce a new particle filter based tracking method, in which we aim to capture the underlying structure among the particle samples using the proposed similarity graph in a Laplacian group sparse framework, such that the tracking results can be improved. Furthermore, in our tracker, particles contribute with different probabilities in the tracking result with respect to their relative positions in a given frame in regard to the current target object location. In addition, since the new target object can be well modelled by the most recent tracking results, we prefer to utilise the particle samples that are highly associated to the preceding tracking results. We demonstrate that the proposed formulation can be efficiently solved using the Accelerated Proximal method with just a small number of iterations. The proposed approach has been extensively evaluated on 12 challenging video sequences. Experimental results compared to the state-of-the-art methods demonstrate the merits of the proposed tracker.

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Notes

  1. 1.

    \(m=1024-dim\) Gray scale based features.

  2. 2.

    \(\mu _{k}\) is conventionally set to \(\frac{2}{k+1}\).

  3. 3.

    We denote \(\widetilde{c}_{i}\) as a discriminative feature, then we build a similarity graph by considering each point as a vertex and assigning the connection weight between the node \(i\) and \(j\) as \(\left| \widetilde{c}_{ij} \right| \).

  4. 4.

    We consider every \(n=5\) frames.

  5. 5.

    This is the element-wise product of two (\(1 \times N\)) matrices. \(N\) is the number of sampled particles.

  6. 6.

    https://sites.google.com/site/trackerbenchmark/benchmarks/v10 .

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Author information

Authors and Affiliations

  1. Vision and Sensing, HCC Lab, ESTeM, University of Canberra, Canberra, Australia

    Behzad Bozorgtabar & Roland Goecke

  2. IHCC, RSCS, CECS, Australian National University, Canberra, Australia

    Roland Goecke

Authors
  1. Behzad Bozorgtabar
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  2. Roland Goecke
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Corresponding author

Correspondence to Behzad Bozorgtabar .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Bozorgtabar, B., Goecke, R. (2015). Enhanced Laplacian Group Sparse Learning with Lifespan Outlier Rejection for Visual Tracking. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_37

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  • DOI: https://doi.org/10.1007/978-3-319-16814-2_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16813-5

  • Online ISBN: 978-3-319-16814-2

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