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Robust visual tracking combining global and local appearance models

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Published:30 December 2010Publication History

ABSTRACT

In this paper, we present a robust visual tracking method combining global and local appearance models. We model the object to be tracked with a RGB color histogram and multiple histograms of oriented gradients (HOG). Modeling object using only the former, a global appearance model, is widely used in visual tracking. However, it suffers many challenges such as illumination changes and pose changes and so on. In order to overcome this problem, we also model the object with multiple block based HOG histograms. The HOG histogram is a local appearance model and can effectively represent the shape information of the object which also gain increasing interests in computer vision especially in pedestrian detection. These two appearance models are complementary and used in the particle filter tracking framework. We test the performance of the proposed method on several challenging sequences, which verifies that our method outperforms the standard particle filter and achieves significant improvement.

References

  1. A. Adam, E. Rivlin, and I. Shimshoni. Robust fragments-based tracking using the integral histogram. In CVPR, pages 798--805, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, 20(2):174--188, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, pages 886--893, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. R. Hess and A. Fern. Discriminatively trained particle filters for complex multi-object tracking. In CVPR, pages 240--247, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  5. Z. Khan, T. Balch, and F. Dellaert. Mcmc-based particle filtering for tracking a variable number of interacting targets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(11):1805--1819, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. K. Okuma, A. Taleghani, N. de Freitas, J. J. Little, and D. G. Lowe. A boosted particle filter: multitarget detection and tracking. In ECCV, pages 28--39, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  7. X. Wang, T. Han, and S. Yan. An hog-lbp human detector with partial occlusion handling. In ICCV, pages 1--8, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  8. M. Yang, J. Yuan, and Y. Wu. Spatial selection for attentional visual tracking. In CVPR, pages 1--8, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  9. T. Yu and Y. Wu. Decentralized multiple target tracking using netted collaborative autonomous trackers. In CVPR, pages 939--946, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Robust visual tracking combining global and local appearance models

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      • Published in

        cover image ACM Other conferences
        ICIMCS '10: Proceedings of the Second International Conference on Internet Multimedia Computing and Service
        December 2010
        218 pages
        ISBN:9781450304603
        DOI:10.1145/1937728
        • General Chairs:
        • Yong Rui,
        • Klara Nahrstedt,
        • Xiaofei Xu,
        • Program Chairs:
        • Hongxun Yao,
        • Shuqiang Jiang,
        • Jian Cheng

        Copyright © 2010 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 30 December 2010

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