Discrete Dynamics in Nature and Society
Volume 2012 (2012), Article ID 318305, 14 pages
Research Article

Fast Pedestrian Recognition Based on Multisensor Fusion

1State Key Laboratory of Automobile Dynamic Simulation, Jilin University, Changchun 130022, China
2College of Transportation, Jilin University, Changchun 130022, China

Received 11 September 2012; Revised 10 November 2012; Accepted 21 November 2012

Academic Editor: Wuhong Wang

Copyright © 2012 Hongyu Hu 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.


A fast pedestrian recognition algorithm based on multisensor fusion is presented in this paper. Firstly, potential pedestrian locations are estimated by laser radar scanning in the world coordinates, and then their corresponding candidate regions in the image are located by camera calibration and the perspective mapping model. For avoiding time consuming in the training and recognition process caused by large numbers of feature vector dimensions, region of interest-based integral histograms of oriented gradients (ROI-IHOG) feature extraction method is proposed later. A support vector machine (SVM) classifier is trained by a novel pedestrian sample dataset which adapt to the urban road environment for online recognition. Finally, we test the validity of the proposed approach with several video sequences from realistic urban road scenarios. Reliable and timewise performances are shown based on our multisensor fusing method.