Laser-based detection and tracking of multiple people in crowds

  • Authors:

  • Jinshi Cui;Hongbin Zha;Huijing Zhao;Ryosuke Shibasaki

  • Affiliations:

  • National Laboratory on Machine Perception, Peking University, Beijing, China;National Laboratory on Machine Perception, Peking University, Beijing, China;Centre for Spatial Information Science, University of Tokyo, Tokyo, Japan;Centre for Spatial Information Science, University of Tokyo, Tokyo, Japan

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2007

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Abstract

Laser-based people tracking systems have been developed for mobile robotic, and intelligent surveillance areas. Existing systems rely on laser point clustering method to extract object locations. However, for dense crowd tracking, laser points of different objects are often interlaced and undistinguishable due to measurement noise and they can not provide reliable features. It causes current systems quite fragile and unreliable. This paper presents a novel and robust laser-based dense crowd tracking method. Firstly, we introduce a stable feature extraction method based on accumulated distribution of successive laser frames. With this method, the noise that generates split and merged measurements is smoothed away and the pattern of rhythmic swing legs is utilized to extract each leg of persons. And then, a region coherency property is introduced to construct an efficient measurement likelihood model. The final tracker is based on the combination of independent Kalman filter and Rao-Blackwellized Monte Carlo data association filter (RBMC-DAF). In real experiments, we obtain raw data from multiple registered laser scanners, which measure two legs for each people on the height of 16cm from horizontal ground. Evaluation with real data shows that the proposed method is robust and effective. It achieves a significant improvement compared with existing laser-based trackers. In addition, the proposed method is much faster than previous works, and can overcome tracking errors resulted from mixed data of two closely situated persons.