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A Stereo-based Pedestrian Detection System for Smart Intersection

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Yunyun Cao, Sugiri Pranata, Makoto Yasugi, Zhiheng Niu, Hirofumi Nishimura
19th ITS World Congress, Vienna, Austria, 22-26 October, 2012

This paper introduces a stereo-based pedestrian detection system for smart intersection applications. Promising for real-time usage, a detector with sliding window approach is employed. In order to reduce scanning time, stereo cameras are utilized. Firstly, the system generates a disparity image from calibrated left and right images. Then, searching regions of interest (ROI) for pedestrians are obtained by clustering the disparity information. Finally, a raster scan is applied to all of the ROI and their scaled copies. For distance measurement, the authors developed a fast and accurate algorithm called Inverted Phase Filter (IPF), which can measure the disparity at sub-pixel level fast. Furthermore, for pedestrian classification, a novel feature – Staggered multi-scale LBP (Local Binary Pattern) histogram – is proposed. Evaluation results show that the proposed feature outperforms benchmarks like HOG and CoHOG on Panasonic night-time dataset and Daimler Chrysler dataset.

Link: https://trid.trb.org/view/1277769