DocumentCode :
3213774
Title :
Real-time vehicle detection using Haar-SURF mixed features and gentle AdaBoost classifier
Author :
Sun Shujuan ; Xu Zhize ; Wang Xingang ; Huang Guan ; Wu Wenqi ; Xu De
Author_Institution :
Inst. of Autom., Beijing, China
fYear :
2015
fDate :
23-25 May 2015
Firstpage :
1888
Lastpage :
1894
Abstract :
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.
Keywords :
Haar transforms; image classification; learning (artificial intelligence); object detection; road vehicles; traffic engineering computing; Android mobile; Haar-SURF mixed features; ROI; cascade classifier; decision classifier; gentle AdaBoost classifier; intelligent driver systems; lane detection; on-road vehicle detection; real-time vehicle detection; search space; Classification algorithms; Databases; Feature extraction; Testing; Training; Vehicle detection; Vehicles; Gentle AdaBoost; Haar-like features; SURF descriptor; Vehicle Detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location :
Qingdao
Print_ISBN :
978-1-4799-7016-2
Type :
conf
DOI :
10.1109/CCDC.2015.7162227
Filename :
7162227
Link To Document :
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