Laser-based detection and tracking of multiple people in crowds

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-...

• Type: Journal
• Year: 2007
• Where: CVIU
• Authors: Jinshi Cui, Hongbin Zha, Huijing Zhao, Ryosuke Shibasaki
• Keywords: CVIU 2007, Dense Crowd, Dense Crowd Tracking, Laser Points