Multi-Camera Multi-Person Tracking and Re-Identification in an Operating Room

Haowen Hu, Ryo Hachiuma, Hideo Saito, Yoshifumi Takatsume, Hiroki Kajita

Research output: Contribution to journalArticlepeer-review

Abstract

Multi-camera multi-person (MCMP) tracking and re-identification (ReID) are essential tasks in safety, pedestrian analysis, and so on; however, most research focuses on outdoor scenarios because they are much more complicated to deal with occlusions and misidentification in a crowded room with obstacles. Moreover, it is challenging to complete the two tasks in one framework. We present a trajectory-based method, integrating tracking and ReID tasks. First, the poses of all surgical members captured by each camera are detected frame-by-frame; then, the detected poses are exploited to track the trajectories of all members for each camera; finally, these trajectories of different cameras are clustered to re-identify the members in the operating room across all cameras. Compared to other MCMP tracking and ReID methods, the proposed one mainly exploits trajectories, taking texture features that are less distinguishable in the operating room scenario as auxiliary cues. We also integrate temporal information during ReID, which is more reliable than the state-of-the-art framework where ReID is conducted frame-by-frame. In addition, our framework requires no training before deployment in new scenarios. We also created an annotated MCMP dataset with actual operating room videos. Our experiments prove the effectiveness of the proposed trajectory-based ReID algorithm. The proposed framework achieves 85.44% accuracy in the ReID task, outperforming the state-of-the-art framework in our operating room dataset.

Original languageEnglish
Article number219
JournalJournal of Imaging
Volume8
Issue number8
DOIs
Publication statusPublished - 2022 Aug

Keywords

  • human re-identification
  • multi-camera multi-person
  • operating room
  • pedestrian tracking
  • trajectory

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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