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A Hybrid Real-Time Visual Tracking Using Compressive RGB-D Features

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Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9474))

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Abstract

The online multi-instance learning tracking (MIL) algorithm is known for its ability of alleviating tracking drift by training classifiers with positive and negative bag. However, the increased computational complexity results in time consuming due to the lack of consideration of sampling importance when collecting training samples. Additionally, the MIL method, as a 2D feature-based tracking algorithm, performs unsteadily when the object changes poses or rotates seriously. In this paper, a histogram-based feature similarity measurement is employed as a weighting strategy to select positive samples. Benefited from profitable depth information, the tracking algorithm we proposed achieves higher tracking performance. For computational efficiency, a compressive sensing method is adopted to extract features and reduce dimensionality. Experimental results demonstrate that our algorithm is better in robustness, accuracy, efficiency than three state-of-the-art methods on challenging video sequences.

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Authors and Affiliations

  1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

    Mengyuan Zhao

  2. College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China

    Heng Luo

  3. Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, 53211, USA

    Ahmad P. Tafti

  4. Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China

    Yuanchang Lin & Guotian He

Authors
  1. Mengyuan Zhao
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  2. Heng Luo
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  3. Ahmad P. Tafti
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  4. Yuanchang Lin
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  5. Guotian He
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Corresponding author

Correspondence to Guotian He .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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© 2015 Springer International Publishing Switzerland

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Zhao, M., Luo, H., Tafti, A.P., Lin, Y., He, G. (2015). A Hybrid Real-Time Visual Tracking Using Compressive RGB-D Features. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_51

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  • DOI: https://doi.org/10.1007/978-3-319-27857-5_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27856-8

  • Online ISBN: 978-3-319-27857-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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