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Salient Superpixel Visual Tracking with Coarse-to-Fine Segmentation and Manifold Ranking

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Advances in Brain Inspired Cognitive Systems (BICS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10989))

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Abstract

We propose a novel salient superpixel based tracking algorithm using Coarse-to-Fine segmentation on graph model, where target state is estimated by a combination of pixel-level cues and middle-level cues to achieve accurate target appearance model. We exploit temporal optical flow and color distribution characteristics as coarse grained information from pixel-level processing, and propagate to fine-grained superpixels to improve initial target appearance segmentation from bounding box annotations. Our algorithm constructs a graph model with manifold ranking by improved superpixels to estimate the saliency of target foreground and background in subsequent frames. The tracking result is located by calculating the weight of multi-scale box, where the weight depends on the similarity of scores of foreground and background superpixels in the scale box. We compared our algorithm with the existing techniques in OTB100 dataset, and achieved substantially better performance.

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Acknowledgment

This research is supported by National Natural Science Foundation of China (61772144, 61672008), Innovation Research Project of Education Department of Guangdong Province (Natural Science) (2016KTSCX077), Foreign Science and Technology Cooperation Plan Project of Guangzhou Science Technology and Innovation Commission (201807010059), Guangdong Provincial Application-oriented Technical Research and Development Special Fund Project (2016B010127006), the Natural Science Foundation of Guangdong Province (2016A030311013), and the Scientific and Technological Projects of Guangdong Province (2017A050501039). The corresponding author is Huimin Zhao.

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Authors and Affiliations

  1. School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China

    Jin Zhan & Huimin Zhao

  2. The Guangzhou Key Laboratory of Digital Content Processing and Security Technologies, Guangzhou, China

    Jin Zhan & Huimin Zhao

Authors
  1. Jin Zhan
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  2. Huimin Zhao
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Corresponding author

Correspondence to Huimin Zhao .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Jinchang Ren

  2. Edinburgh Napier University, Edinburgh, UK

    Amir Hussain

  3. Northwestern Polytechnical University, Xi’an, China

    Jiangbin Zheng

  4. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  5. Anhui University, Hefei, China

    Bin Luo

  6. Guangdong Polytechnic Normal University, Guangzhou, China

    Huimin Zhao

  7. Northwestern Polytechnical University, Xi’an, China

    Xinbo Zhao

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Zhan, J., Zhao, H. (2018). Salient Superpixel Visual Tracking with Coarse-to-Fine Segmentation and Manifold Ranking. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_42

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  • DOI: https://doi.org/10.1007/978-3-030-00563-4_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00562-7

  • Online ISBN: 978-3-030-00563-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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