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Illumination invariant object tracking with adaptive sparse representation

  • Intelligent and Information Systems
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Abstract

Since the introduction of the sparse representation-based tracking method named ℓ1 tracker, there have been further studies into this tracking framework with promised results in challenging video sequences. However, in the situation of large illumination changes and shadow casting, the tracked object cannot be modeled efficiently by sparse representation templates. To overcome this problem, we propose a new illumination invariant tracker based on photometric normalization techniques and the sparse representation framework. With photometric normalization methods, we designed a new illumination invariant template presentation for tracking that eliminates the illumination influences, such as brightness variation and shadow casting. For a higher tracking accuracy, we introduced a strategy that adaptively selects the optimum template presentation at the update step of the tracking process. The experiments show that our approach outperforms the previous ℓ1 and some state-of-the-art algorithms in tracking sequences with severe illumination effects.

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Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Chonnam National University, 300, Yong-Bong-Dong, Kwangju, Korea

    Vo Quang Nhat & Gueesang Lee

Authors
  1. Vo Quang Nhat
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  2. Gueesang Lee
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Corresponding author

Correspondence to Gueesang Lee.

Additional information

Recommended by Associate Editor Dong-Joong Kang under the direction of Editor Myotaeg Lim.

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (2013-056480 and 2013-006535). Also this research was supported by the MSIP (Ministry of Science, ICT & Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2013-H0301-13-3005) supervised by the NIPA (National IT Industry Promotion Agency).

Vo Quang Nhat received his B.S. degree in Information Technology from the University of Science Ho Chi Minh City, Vietnam in 2010, and his M.S. degree in Electronics and Computer Engineering from the Chonnam National University, Korea in 2013. He is currently a Ph.D. student at the Department of Chonnam National University, Korea. His interesting studies are in multimedia and image processing, vision tracking, and pattern recognition.

Gueesang Lee received his B.S. degree in Electrical Engineering and his M.S. degree in Computer Engineering from Seoul National University, Korea in 1980 and 1982, respectively. He received his Ph.D. degree in Computer Science from Pennsylvania State University in 1991. He is currently a professor of the Department of Electronics and Computer Engineering in Chonnam National University, Korea. His research interests are mainly in the field of image processing, computer vision and video technology.

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Nhat, V.Q., Lee, G. Illumination invariant object tracking with adaptive sparse representation. Int. J. Control Autom. Syst. 12, 195–201 (2014). https://doi.org/10.1007/s12555-013-0077-x

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  • DOI: https://doi.org/10.1007/s12555-013-0077-x

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