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

  • Nanyang Liu
  • Rong Xiong
  • Qianshan Li
  • Yue Wang
Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

human tracking multi-sensor fusion SJPDAF 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nanyang Liu
    • 1
  • Rong Xiong
    • 1
  • Qianshan Li
    • 1
  • Yue Wang
    • 1
  1. 1.State Key Laboratory of Industrial Control TechnologyZhejiang UniversityHangzhouChina

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