Multi-modal tracking of people using laser scanners and video camera

Abstract

Inspite extensive research on visual tracking of multiple people in computer vision area, the robustness and usability of visual trackers are still discouraging. Recently, a few laser-based detection and tracking methods have been developed in robotics area. However, poor features provided by laser data make the tracker fail in many situations. In this paper, we present a novel method that aims at reliably detecting and tracking multiple people in an open area. Multiple laser scanners and one camera are used as input sensors. In detection stage, laser-based detection algorithm captures newly appeared people and initializes the mean-shift-based visual tracker. In tracking stage, laser-based feet trajectory tracking result and visual body region tracking result are combined with a decision-level Bayesian fusion method. The experimental results demonstrate reliable and real-time performance of the method.