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Optical Engineering

Stereo vision-based vehicle detection using a road feature and disparity histogram
Author(s): ChungHee Lee; Young-Chul Lim; Soon Kwon; Jonghun Lee
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Paper Abstract

This paper presents stereo vision-based vehicle detection approach on the road using a road feature and disparity histogram. It is not easy to detect only vehicles robustly on the road in various traffic situations, for example, a nonflat road or a multiple-obstacle situation. This paper focuses on the improvement of vehicle detection performance in various real traffic situations. The approach consists of three steps, namely obstacle localization, obstacle segmentation, and vehicle verification. First, we extract a road feature from v-disparity maps binarized using the most frequent values in each row and column, and adopt the extracted road feature as an obstacle criterion in column detection. However, many obstacles still coexist in each localized obstacle area. Thus, we divide the localized obstacle area into multiple obstacles using a disparity histogram and remerge the divided obstacles using four criteria parameters, namely the obstacle size, distance, and angle between the divided obstacles, and the difference of disparity values. Finally, we verify the vehicles using a depth map and gray image to improve the performance. We verify the performance of our proposed method by conducting experiments in various real traffic situations. The average recall rate of vehicle detection is 95.5%.

Paper Details

Date Published: 1 February 2011
PDF: 23 pages
Opt. Eng. 50(2) 027004 doi: 10.1117/1.3535590
Published in: Optical Engineering Volume 50, Issue 2
Show Author Affiliations
ChungHee Lee, Daegu Gyeongbuk Institute of Science & Technology (Korea, Republic of)
Young-Chul Lim, Daegu Gyeongbuk Institute of Science & Technology (Korea, Republic of)
Soon Kwon, Daegu Gyeongbuk Institute of Science & Technology (Korea, Republic of)
Jonghun Lee, Daegu Gyeongbuk Institute of Science & Technology (Korea, Republic of)


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