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Multi-class Target Tracking Using the Semantic PHD Filter

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Robotics Research (ISRR 2019)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 20))

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Abstract

In order for a mobile robot to be able to effectively operate in complex, dynamic environments it must be capable of understanding both where and what the objects around them are. In this paper we introduce the semantic probability hypothesis density (SPHD) filter, which allows robots to simultaneously track multiple classes of targets despite measurement uncertainty, including false positive detections, false negative detections, measurement noise, and target misclassification. The SPHD filter is capable of incorporating a different motion model for each type of target and of functioning in situations where the number of targets is unknown and time-varying. We demonstrate the efficacy of the SPHD filter via simulations with multiple target types containing both static and dynamic targets. We show that the SPHD filter performs better than a collection of PHD filters running in parallel, one for each target class.

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Acknowledgements

This work was funded by NSF grant IIS-1830419 and the Amazon Research Awards program. We would like to thank Zhijia Chen from Temple University for assistance with the data processing work.

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Authors and Affiliations

  1. Temple University, Philadelphia, PA, 19122, USA

    Jun Chen & Philip Dames

Authors
  1. Jun Chen
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  2. Philip Dames
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Corresponding author

Correspondence to Philip Dames .

Editor information

Editors and Affiliations

  1. Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Baden-Württemberg, Germany

    Tamim Asfour

  2. Department of Information Technology and Human Factors, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan

    Eiichi Yoshida

  3. Seoul National University, Seoul, Korea (Republic of)

    Jaeheung Park

  4. Jacobs School of Engineering, Institute for Contextual Robotics, San Diego, CA, USA

    Henrik Christensen

  5. Department of Computer Science, Stanford University, Stanford, CA, USA

    Oussama Khatib

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Chen, J., Dames, P. (2022). Multi-class Target Tracking Using the Semantic PHD Filter. In: Asfour, T., Yoshida, E., Park, J., Christensen, H., Khatib, O. (eds) Robotics Research. ISRR 2019. Springer Proceedings in Advanced Robotics, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-030-95459-8_32

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