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Real-Time RGBD Object Tracking via Collaborative Appearance and Motion Models

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 874))

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Abstract

Visual object tracking remains an active and challenging topic in computer vision due to a great variety of intricate factors such as illumination variation, object deformation and background clutter. Recent research efforts have achieved impressive success in object tracking, but they commonly have to utilize complicated models requiring high computation cost, which renders these methods hardly suitable for many applications. Considering depth information of the scene can provide effective complement to color images, in this paper, we propose a novel and efficient method for tracking an object in RGBD videos by using collaborative appearance and motion models. Experimental results demonstrate that our method achieves superior tracking performance over several state-of-the-methods while running efficiently.

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Acknowledgement

This research is supported by the National Natural Science Foundation of China (61402120, 61772144), the Natural Science Foundation of Guangdong Province (2014A030310348), the Characteristic Innovation (Natural Science) Program of the Education Department of Guangdong Province (2016KTSCX077), and the Startup Program in Guangdong University of Foreign Studies (299-X5122029). The corresponding author is Hefeng Wu.

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Authors and Affiliations

  1. School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China

    Danxian Chen, Zhanming Liu & Hefeng Wu

  2. Institute of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou, China

    Jin Zhan

Authors
  1. Danxian Chen
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  2. Zhanming Liu
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  3. Hefeng Wu
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  4. Jin Zhan
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Corresponding author

Correspondence to Hefeng Wu .

Editor information

Editors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. Jiangxi University of Science and Technology, Ganzhou, Jiangxi, China

    Wei Li

  3. Chemical and Petroleum Engineering, University of Calgary, Calgary, Alberta, Canada

    Zhangxing Chen

  4. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

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Chen, D., Liu, Z., Wu, H., Zhan, J. (2018). Real-Time RGBD Object Tracking via Collaborative Appearance and Motion Models. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 874. Springer, Singapore. https://doi.org/10.1007/978-981-13-1651-7_40

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  • DOI: https://doi.org/10.1007/978-981-13-1651-7_40

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1650-0

  • Online ISBN: 978-981-13-1651-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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