Fusion of Detection and Matching Based Approaches for Laser Based Multiple People Tracking

Abstract

Most of visual tracking algorithms have been achieved by matching-based searching strategies or detection-based data association algorithms. In this paper, our objective is to analysis laser scan image sequences to track multiple people in a crowded environment. Due to the poor features provided by laser scan images, neither of the above two approaches can achieves good tracking. To address the problem, we propose a novel multiple-target tracking algorithm fusing both detection and matching based strategies. First, target to detected measurement data association is incorporated to the joint state proposal, to form a mixture proposal that combines information from the dynamic model and the detected measurements. And then, we utilize a MCMC sampling step to obtain a more efficient multi-target filter. Our approach has been applied to the real laser scan image data. Evaluations show that the proposed method is a robust and effective multi-target tracking algorithm.