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Level Set Based Online Visual Tracking via Convolutional Neural Network

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10636))

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Abstract

In this paper, we propose a level set tracking algorithm, which integrates the information of the original frame and the confidence predicted by the deep feature based detector. First, we extract features from convolutional neural network and select part of them to avoid redundancy. Secondly, the features are used to generate a confidence map of the tracked object through the detector. And then the confidence along with the original frame is applied in level set model to acquire the segmentation result. We introduce an outlier rejection scheme to further improve the result. Finally, updating is employed to the detector to adapt to the changes in the video. One important contribution of our work is to use the deep features in confidence prediction, particularly the usage of low-level features in the neural network. Experimental results show that our model delivers a better performance than the state-of-the-art on a series of challenging videos.

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Acknowledgments

This work is supported by National Natural Science Foundation of China under grant 61370133 and grant 61672095.

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Authors and Affiliations

  1. Beijing Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Xiaodong Ning & Lixiong Liu

Authors
  1. Xiaodong Ning
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  2. Lixiong Liu
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Corresponding author

Correspondence to Lixiong Liu .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Ning, X., Liu, L. (2017). Level Set Based Online Visual Tracking via Convolutional Neural Network. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_29

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  • DOI: https://doi.org/10.1007/978-3-319-70090-8_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70089-2

  • Online ISBN: 978-3-319-70090-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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