Real-time Vehicle Detection using Haar-SURF Mixed Features and Gentle AdaBoost Classifier

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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
会议日期	23-25 May 5015
会议地点	Qingdao, China
关键词	Vehicles Training Vehicle Detection Feature Extraction Databases Classification Algorithms Testing
英文摘要	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.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/19772]
专题	精密感知与控制研究中心_精密感知与控制
作者单位	中国科学院自动化研究所
推荐引用方式 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]. 见:. Qingdao, China. 23-25 May 5015.