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Stereo vision-based vehicle detection using a road feature and disparity histogram

Title
Stereo vision-based vehicle detection using a road feature and disparity histogram
Authors
Lee, CH[Lee, Chung-Hee]Lim, YC[Lim, Young-Chul]Kwon, S[Kwon, Soon]Lee, JH[Lee, Jong-Hun]
DGIST Authors
Lee, CH[Lee, Chung-Hee]; Lim, YC[Lim, Young-Chul]; Kwon, S[Kwon, Soon]; Lee, JH[Lee, Jong-Hun]
Issue Date
2011-02
Citation
Optical Engineering, 50(2)
Type
Article
Article Type
Article
Keywords
Criteria ParametersDepth MapDisparity HistogramFeature ExtractionFrequent ValuesGraphic MethodsGray ImageObstacle SegmentationReal TrafficRecall RateRoad Feature ExtractionRoads and StreetsStereo VisionTraffic SituationsVehicle DetectionVehiclesVision-Based Vehicle Detection
ISSN
0091-3286
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%. © 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).
URI
http://hdl.handle.net/20.500.11750/2489
DOI
10.1117/1.3535590
Publisher
SPIE
Files:
There are no files associated with this item.
Collection:
Intelligent Devices and Systems Research Group1. Journal Articles


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