Multi-person detecting and tracking based on RGB-D sensor for a robot vision system

Abstract

In this paper, we address the problem of automatically detecting and tracking a variable number of objects in complex scenes using a RGB-D sensor on the robot system. We propose a novel approach for multi-object detecting by fusing RGB information and depth information. Meanwhile, this paper presents a robust multi-cue approach for multi-object tracking. A spatiotemporal object representation is proposed, which combines a generative colour model and a discriminative texture classifier. We employ a Bayesian framework based on particle filtering to achieve integrated object detection and tracking from a robot vision system. The experimental results show that the proposed method yields good tracking performance in real world environment.