Closed-Loop Person Tracking and Detection

  • Authors:
  • Affiliations:
  • Venue:
  • CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
  • Year:
  • 2004

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Abstract

We present a system that detects people in indoor scenesby modeling the motion history of foreground blobs, ratherthan their shape or appearance. The system tracks all foregroundblobs over time with a multi-hypothesis tracker, andconsiders a blob to be a person if it exhibited sufficient autonomousmovement in the course of its tracking history.This way, people can be correctly classi.ed even if they areseen in a wide range of body poses, if they remain still for along time, or if they change appearance by taking off a coat.Evaluation on over 1h of video demonstrated good performancefor both heuristic and decision tree based classification.