Laser-based Interacting People Tracking Using Multi-level Observations

Abstract

Laser based people tracking systems have been developed for mobile robotics and intelligent surveillance areas. Existing systems rely on simple laser point clustering methods to extract object locations. However, when dealing with multiple interacting people, laser points of different persons are often interlaced and undistinguishable due to measurement noise and they can not provide reliable features. It causes current systems quite fragile and unreliable. In this paper, we try to explore potentials from multi-level observations including weakly detected features, stably extracted features and foreground points. For inference, detection incorporated joint particle filter is used. And stably extracted features are utilized to properly estimate parameters of dynamic model for each target. In real experiments, we obtain raw data from multiple registered laser scanners, which measure two legs for each people. Evaluations with real data show that the proposed method is more robust and effective than existing approaches