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Tracking Based on Unit-Linking Pulse Coupled Neural Network Image Icon and Particle Filter

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Advances in Neural Networks – ISNN 2016 (ISNN 2016)

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Abstract

Visual tracking is a challenging problem in computer vision. Many visual trackers either rely on luminance information or other simple color representations for image description. This paper introduces a tracking algorithm using unit-linking PCNN (Pulse Coupled Neural Network) image icon and particle filter. This approach has the translation, rotation, and scale invariance for using unit-linking PCNN image icon as the features. The experimental results show the proposed approach is with 16.43 % higher median distance precision than the color gradient-based tracker. This unit-linking PCNN image icon-based particle filter tracker can better solve the problems caused by partial occlusions, or out-of-plane rotation, or scale variation, or non-rigid object deformation, or fast motion.

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Acknowledgments

This work was supported by National Natural Science Foundation of China under grant 61371148.

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Authors and Affiliations

  1. Department of Electronic Engineering, Fudan University, Shanghai, 200433, China

    Hang Liu & Xiaodong Gu

  2. Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), Fudan University, Shanghai, 200433, China

    Hang Liu & Xiaodong Gu

  3. Research Center of Smart Networks and Systems, Fudan University, Shanghai, 200433, China

    Hang Liu & Xiaodong Gu

Authors
  1. Hang Liu
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  2. Xiaodong Gu
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Corresponding author

Correspondence to Xiaodong Gu .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. Huazhong University of Science and Tech., Wuhan, Jiangsu, China

    Qingshan Liu

  3. Russian Academy of Sciences, SPIIRAS, St. Petersburg, Russia

    Andrey Ronzhin

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Liu, H., Gu, X. (2016). Tracking Based on Unit-Linking Pulse Coupled Neural Network Image Icon and Particle Filter. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_72

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  • DOI: https://doi.org/10.1007/978-3-319-40663-3_72

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40662-6

  • Online ISBN: 978-3-319-40663-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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