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Spatial-Temporal Saliency Feature Extraction for Robust Mean-Shift Tracker

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Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8834))

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Abstract

Robust object tracking in crowded and cluttered dynamic scenes is a very difficult task in robotic vision due to complex and changeable environment and similar features between the background and foreground. In this paper, a saliency feature extraction method is fused into mean-shift tracker to overcome above difficulties. First, a spatial-temporal saliency feature extraction method is proposed to suppress the interference of the complex background. Furthermore, we proposed a saliency evaluation method by fusing the top-down visual mechanism to enhance the tracking performance. Finally, the efficiency of the saliency features based mean-shift tracker is validated through experimental results and analysis.

This work is supported in part by the National Key Technology R&D Program of China #2012BAI34B02, National Natural Science Foundation of China(NNSF) Grants #61101221, #60725310, #61033011.

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Author information

Authors and Affiliations

  1. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, ZhongGuanCunDong Road, HaiDian District, Beijing, China

    Suiwu Zheng, Linshan Liu & Hong Qiao

Authors
  1. Suiwu Zheng
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  2. Linshan Liu
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  3. Hong Qiao
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology Building, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Chu Kiong Loo

  2. Department of Electronics and Communication Engineering,College of Engineering, Jalan IKRAM-UNITEN, Universiti Tenaga Nasional, 43009, Kajang, Selangor, Malaysia

    Keem Siah Yap

  3. School of Engineering and Information Technology, Murdoch University, South St, 6150, Murdoch, Western Australia, Australia

    Kok Wai Wong

  4. Department of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, 120-749, Seoul, South Korea

    Andrew Teoh

  5. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Ren’ai Road 111, SIP 215123, Suzhou, Jiangsu Province, China

    Kaizhu Huang

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© 2014 Springer International Publishing Switzerland

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Zheng, S., Liu, L., Qiao, H. (2014). Spatial-Temporal Saliency Feature Extraction for Robust Mean-Shift Tracker. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_24

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  • DOI: https://doi.org/10.1007/978-3-319-12637-1_24

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12636-4

  • Online ISBN: 978-3-319-12637-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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