|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
|会议名称||Control and Decision Conference
|会议日期||23-25 May 5015
|摘要||On-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.|
Sun Shujuan,Xu Zhize,Wang Xingang,et al. Real-time Vehicle Detection using Haar-SURF Mixed Features and Gentle AdaBoost Classifier[C],2015.