Lin, B.-F.B.-F.LinLin, Y.-M.Y.-M.LinLI-CHEN FUHsiao, P.-Y.P.-Y.HsiaoChuang, L.-A.L.-A.ChuangHuang, S.-S.S.-S.HuangLo, M.-F.M.-F.Lo2018-09-102018-09-10201215249050https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862805474&doi=10.1109%2fTITS.2011.2182649&partnerID=40&md5=b0811ed5777040afcd2f3b3b14aac689http://scholars.lib.ntu.edu.tw/handle/123456789/372655Changing lanes while having no information about the blind spot area can be dangerous. We propose a vision-based vehicle detection system for a lane changing assistance system to monitor the potential sedan vehicle in the blind-spot area. To serve our purpose, we select adequate features, which are directly obtained from vehicle images, to detect possible vehicles in the blind-spot area. This is challenging due to the significant change in the view angle of a vehicle along with its location throughout the blind-spot area. To cope with this problem, we propose a method to combine two kinds of part-based features that are related to the characteristics of the vehicle, and we build multiple models based on different viewpoints of a vehicle. The location information of each feature is incorporated to help construct the detector and estimate the reasonable position of the presence of the vehicle. The experiments show that our system is reliable in detecting various sedan vehicles in the blind-spot area. © 2012 IEEE.Blind-spot area; feature integration; spatial relationship; vehicle detectionBlind spots; Blind-spot area; Changing lanes; Edge features; Feature integration; Lane changing assistance system; Location information; Multiple models; Spatial relationships; Vehicle detection; Vehicle images; Vision-based vehicle detection; Computer applications; Intelligent systems; Vehiclesjournal article10.1109/TITS.2011.21826492-s2.0-84862805474