Cooperative people tracking by distributed cameras network

Yi Chang Wu, Ching Han Chen, Yao Te Chiu, Pi Wei Chen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In the application of video surveillance, reliable people detection and tracking are always challenging tasks. The conventional single-camera surveillance system may encounter difficulties such as narrow-angle of view and dead space. In this paper, we proposed multi-cameras network architecture with an inter-camera hand-off protocol for cooperative people tracking. We use the YOLO model to detect multiple people in the video scene and incorporate the particle swarm optimization algorithm to track the person movement. When a person leaves the area covered by a camera and enters an area covered by another camera, these cameras can exchange relevant information for uninterrupted tracking. The motion smoothness (MS) metrics is proposed for evaluating the tracking quality of multi-camera networking system. We used a three-camera system for two persons tracking in overlapping scene for experimental evaluation. Most tracking person offsets at different frames were lower than 30 pixels. Only 0.15% of the frames showed abrupt increases in offsets pixel. The experiment results reveal that our multi-camera system achieves robust, smooth tracking performance.

Original languageEnglish
Article number1780
JournalElectronics (Switzerland)
Issue number15
StatePublished - 1 Aug 2021


  • Cooperative tracking
  • Gait recognition
  • Multi-camera
  • PSO


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