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Real-Time Grayscale-Thermal Tracking via Laplacian Sparse Representation

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MultiMedia Modeling (MMM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9517))

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Abstract

Grayscale and thermal data can complement to each other to improve tracking performance in some challenging scenarios. In this paper, we propose a real-time online grayscale-thermal tracking method via Laplacian sparse representation in Bayesian filtering framework. Specifically, a generative multimodal feature model is induced by the Laplacian sparse representation, which makes the best use of similarities among local patches to refine their sparse codes, so that different source data can be seamlessly fused for object tracking. In particular, the multimodal feature model encodes both the spatial local information and occlusion handling to improve its robustness. With such feature representation, the confidence of each candidate is computed by the sparse feature similarity with the object template. Given the motion model, object tracking is then carried out in Bayesian filtering framework by maximizing the observation likelihood, i.e., finding the candidate with highest confidence. In addition, to achieve real-time demand in related visual information processing systems, we adopt the reverse representation and the parallel computation to improve tracking efficiency. Extensive experiments on both public and collected grayscale-thermal video sequences demonstrate accuracy and efficiency of the proposed method against other state-of-the-art sparse representation based trackers.

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Acknowledgement

This work was supported by the Development Program (863 Program) of China (No. 2014AA015104) and the Natural Science Foundation of China (No. 61472002).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Anhui University, Hefei, China

    Chenglong Li, Sihan Gao & Jin Tang

  2. School of Data and Computer Science, Sun Yat-sen University, Hefei, China

    Shiyi Hu

Authors
  1. Chenglong Li
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  2. Shiyi Hu
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  3. Sihan Gao
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  4. Jin Tang
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Corresponding author

Correspondence to Jin Tang .

Editor information

Editors and Affiliations

  1. University of Texas at San Antonio, San Antonio, USA

    Qi Tian

  2. Dept. of Information Engineering, University of Trento, Povo, Trento, Italy

    Nicu Sebe

  3. EECS, University of Central Florida, Orlando, Florida, USA

    Guo-Jun Qi

  4. EURECOM, Sophia-Antipolis, France

    Benoit Huet

  5. Hefei University of Technology, Hefei, Anhui, China

    Richang Hong

  6. School of Computing and Information, Hefei University of Technology, Hefei, Anhui, China

    Xueliang Liu

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© 2016 Springer International Publishing Switzerland

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Li, C., Hu, S., Gao, S., Tang, J. (2016). Real-Time Grayscale-Thermal Tracking via Laplacian Sparse Representation. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9517. Springer, Cham. https://doi.org/10.1007/978-3-319-27674-8_6

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  • DOI: https://doi.org/10.1007/978-3-319-27674-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27673-1

  • Online ISBN: 978-3-319-27674-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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