Robust Tracking of Multiple People in Crowds Using Laser Range Scanners

Abstract

Laser based people tracking systems have been developed for mobile robotic or intelligent surveillance areas. Existing systems rely on laser point clustering to extract object locations. However, in a crowded environment, laser points of different objects are often interlaced and undistinguishable and can not provide reliable features. This paper presents a novel and robust laser-based tracking method for people in crowds. Firstly, we propose a stable feature extraction method based on accumulated distribution of successive laser frames. Then a robust tracking filter is proposed based on the combination of independent Bayesian filter and sampling based data association filter. Evaluations with real data show that the proposed method is robust and effective. It achieves a significant improvement compared with existing trackers.