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Human Tracking Using Improved Sample-Based Joint Probabilistic Data Association Filter

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Intelligent Autonomous Systems 12

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 194))

Abstract

The human tracking problem is a hot issue in human-robot interaction, in which a conventional algorithm sample-based joint probabilistic data association filters (SJPDAF) is widely used. In this paper, the algorithm is first extended to the situation of multi-sensor fusion and then accelerated to promote the real-time performance. The simulation and experiments on robots both show good results, reflecting the robust and the accuracy of our improved SJPDAF.

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Author information

Authors and Affiliations

  1. State Key Laboratory of Industrial Control Technology, Zhejiang University, Zheda Road 38, Hangzhou, 310027, China

    Nanyang Liu, Rong Xiong, Qianshan Li & Yue Wang

Authors
  1. Nanyang Liu
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  2. Rong Xiong
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  3. Qianshan Li
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  4. Yue Wang
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Editor information

Editors and Affiliations

  1. , School of Information and Communication, Sungkyunkwan University, 300 Chunchun-Dong Jangan-Ku, Kyunggi-Do, 440-746, Korea, Republic of (South Korea)

    Sukhan Lee

  2. Science and Technology, Daegu Gyeongbuk Institute of, 50-1, Sang-Ri, Hyeonpung-Myeon, Dalseong-Gun, Daegu, 711-873, Korea, Republic of (South Korea)

    Hyungsuck Cho

  3. Konkuk University, Neungdong-ro, Gwangjin-gu 120, Seoul, 143-701, Korea, Republic of (South Korea)

    Kwang-Joon Yoon

  4. , Department of Electronics Engineering, Pusan National University, Geum Jeong-ku, Pusan, 609-735, Korea, Republic of (South Korea)

    Jangmyung Lee

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© 2013 Springer-Verlag Berlin Heidelberg

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Liu, N., Xiong, R., Li, Q., Wang, Y. (2013). Human Tracking Using Improved Sample-Based Joint Probabilistic Data Association Filter. In: Lee, S., Cho, H., Yoon, KJ., Lee, J. (eds) Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, vol 194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33932-5_28

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  • DOI: https://doi.org/10.1007/978-3-642-33932-5_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33931-8

  • Online ISBN: 978-3-642-33932-5

  • eBook Packages: EngineeringEngineering (R0)

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