Authors: Mingxin Jiang; Dusheng Wang; Tianshuang Qiu
Addresses: Faculty of Electronic and Information Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, 223003, China ' School of Information and Communication Engineering, Dalian Nationalities University, Dalian, Liaoning 116600, China ' Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116023, China
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.
Keywords: multi-object detection; multi-cue object representation; particle filtering; RGB-D sensors; multi-person detection; multi-person tracking; robot vision; sensor fusion; RGB data; depth information; colour modelling; texture classification; object detection; object tracking.
International Journal of Embedded Systems, 2017 Vol.9 No.1, pp.54 - 60
Received: 26 Mar 2015
Accepted: 20 Jun 2015
Published online: 21 Jan 2017 *