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Real-time Vehicle Detection using Haar-SURF Mixed Features and Gentle AdaBoost Classifier
Sun Shujuan; Xu Zhize; Wang Xingang; Huang Guan; Wu Wenqi; Xu De
2015
Conference NameControl and Decision Conference
Conference Date23-25 May 5015
Conference PlaceQingdao, China
AbstractOn-road vehicle detection is one of the key techniques in intelligent driver systems and has been an active research area in the past years. Considering the high demand for real-time and robust vehicle detection method, a novel vehicle detection method has been proposed. This paper presents a real-time vehicle detection algorithm which uses cascade classifier and Gentle AdaBoost classifier with Haar-SURF mixed features. We built up a large database including vehicles and non-vehicles for training and testing. A pipeline is then presented to solve the detection problem. Firstly, lane detection is employed to reduce the search space to a ROI. Secondly, the cascade classifier is applied to generate some candidates. Finally, the single decision classifier evaluates the candidates and provides the target vehicle. The experiments and on-road tests prove it to be a real-time and robust algorithm. In addition, we demonstrate the effectiveness and practicability of the algorithm by porting it to an Android mobile.
KeywordVehicles Training Vehicle Detection Feature Extraction Databases Classification Algorithms Testing
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19772
Collection精密感知与控制研究中心_精密感知与控制
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Sun Shujuan,Xu Zhize,Wang Xingang,et al. Real-time Vehicle Detection using Haar-SURF Mixed Features and Gentle AdaBoost Classifier[C],2015.
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