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학술대회 Fast and Reliable Two-wheeler Detection Algorithm for Blind Spot Detection Systems
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저자
백장운, 한병길, 강현우, 정윤수
발행일
201710
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2017, pp.514-517
DOI
https://dx.doi.org/10.1109/ICTC.2017.8191030
협약과제
17ZD1200, 상황인지 스마트카 퓨전 플랫폼 개발 및 지역 부품업체 지원사업, 박미룡
초록
In this paper, we propose a real-Time detection algorithm using a MCT AdaBoost classifier which detects two-wheeler in a blind spot. The proposed algorithm uses a cascade classifier generated by AdaBoost learning based on the MCT feature vector. The MCT AdaBoost classifier is composed of weak classifiers as many as the number of pixels of the detection window, and each pixel becomes a weak classifier. The smaller the detection window, the faster the processing speed, and the larger the detection window, the greater the accuracy. The proposed algorithm uses two classifiers with different detection window sizes. The first classifier generates candidates quickly with a small detection window. The second classifier verifies the generated candidates with a large detection window. Accordingly, the proposed algorithm supports fast and reliable two-wheeler detection. Also, the proposed algorithm uses a wheel classifier in order to detect an adjacent two-wheeler in the blind spot which is well not detected by two-wheeler classifiers. Experimental results show that the proposed algorithm has faster processing speed and higher detection rate than a single classifier without generating candidates.
KSP 제안 키워드
AdaBoost Classifier, Blind spot detection, Cascade Classifier, Detection algorithm, Feature Vector, Intrusion detection system(IDS), Processing speed, Single classifier, Weak classifier, detection rate(DR), real-time detection