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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 150541, 10 pages
http://dx.doi.org/10.1155/2015/150541
Research Article

A Multisource Heterogeneous Data Fusion Method for Pedestrian Tracking

Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China

Received 25 September 2014; Accepted 25 February 2015

Academic Editor: Shueei M. Lin

Copyright © 2015 Zhenlian Shi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Traditional visual pedestrian tracking methods perform poorly when faced with problems such as occlusion, illumination changes, and complex backgrounds. In principle, collecting more sensing information should resolve these issues. However, it is extremely challenging to properly fuse different sensing information to achieve accurate tracking results. In this study, we develop a pedestrian tracking method for fusing multisource heterogeneous sensing information, including video, RGB-D sequences, and inertial sensor data. In our method, a RGB-D sequence is used to position the target locally by fusing the texture and depth features. The local position is then used to eliminate the cumulative error resulting from the inertial sensor positioning. A camera calibration process is used to map the inertial sensor position onto the video image plane, where the visual tracking position and the mapped position are fused using a similarity feature to obtain accurate tracking results. Experiments using real scenarios show that the developed method outperforms the existing tracking method, which uses only a single sensing dataset, and is robust to target occlusion, illumination changes, and interference from similar textures or complex backgrounds.