Employing shadows for multi-person tracking based on a single RGB-D camera

Wei Gai, Meng Qi, Mingcong Ma, Lu Wang, Chenglei Yang, Juan Liu, Yulong Bian, Gerard de Melo, Shijun Liu, Xiangxu Meng

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Although there are many algorithms to track people that are walking, existing methods mostly fail to cope with occluded bodies in the setting of multi-person tracking with one camera. In this paper, we propose a method to use people’s shadows as a clue to track them instead of treating shadows as mere noise. We introduce a novel method to track multiple people by fusing shadow data from the RGB image with skeleton data, both of which are captured by a single RGB Depth (RGB-D) camera. Skeletal tracking provides the positions of people that can be captured directly, while their shadows are used to track them when they are no longer visible. Our experiments confirm that this method can efficiently handle full occlusions. It thus has substantial value in resolving the occlusion problem in multi-person tracking, even with other kinds of cameras.

Original languageAmerican English
Article number1056
JournalSensors (Switzerland)
Issue number4
StatePublished - Feb 2 2020

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering


  • Multi-person tracking
  • Occlusion
  • RGB-D camera
  • Shadow


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