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Visual Tracking by Local Superpixel Matching with Markov Random Field

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Book cover Advances in Multimedia Information Processing - PCM 2016 (PCM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9916))

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Abstract

In this paper, we propose a novel method to track non-rigid and/or articulated objects using superpixel matching and markov random field (MRF). Our algorithm consists of three stages. First, a superpixel dataset is constructed by segmenting training frames into superpixels, and each superpixel is represented by multiple features. The appearance information of target is encoded in the superpixel database. Second, each new frame is segmented into superpixels and then its object-background confidence map is derived by comparing its superpixels with k-nearest neighbors in superpixel dataset. Taking context information into account, we utilize MRF to further improve the accuracy of confidence map. In addition, the local context information is incorporated through a feedback to refine superpixel matching. In the last stage, visual tracking is achieved via finding the best candidate by maximum a posterior estimate based on the confidence map. Experiments show that our method outperforms several state-of-the-art trackers.

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Notes

  1. 1.

    The surrounding region is a square area centered at the location of target \(X_{t}^{c}\), and its side length is equal to \(\lambda _{s}[X_{s}^{t}]^{\frac{1}{2}}\), where \(X_{t}^{c}\) represents the center location of target region \(X_{t}\) and \(X_{t}^{s}\) denotes its size. The parameter \(\lambda _{s}\) is a constant variable, which determines the size of this surrounding region.

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Acknowledgement

This work was primarily supported by Foundation Research Funds for the Central Universities (Program No. 2662016PY008 and Program No. 2662014PY052).

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Authors and Affiliations

  1. College of Engineering, Huazhong Agricultural University, Wuhan, China

    Heng Fan

  2. College of Informatics, Huazhong Agricultural University, Wuhan, China

    Jinhai Xiang & Zhongmin Chen

Authors
  1. Heng Fan
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  2. Jinhai Xiang
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  3. Zhongmin Chen
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Corresponding author

Correspondence to Jinhai Xiang .

Editor information

Editors and Affiliations

  1. Zhengzhou University, Zhengzhou, China

    Enqing Chen

  2. Jiaotong University, Xi’an, China

    Yihong Gong

  3. Zhengzhou University, Zhengzhou, China

    Yun Tie

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Fan, H., Xiang, J., Chen, Z. (2016). Visual Tracking by Local Superpixel Matching with Markov Random Field. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9916. Springer, Cham. https://doi.org/10.1007/978-3-319-48890-5_1

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  • DOI: https://doi.org/10.1007/978-3-319-48890-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48889-9

  • Online ISBN: 978-3-319-48890-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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