Vehicle Detection in Still Images by Using Boosted Local Feature Detector

作     者：Young-joon HAN Hern-soo HAHN 

作者机构：Department of Electronic EngineeringSoongsil UniversitySeoul 156-743South Korea 

出 版 物：《Journal of Measurement Science and Instrumentation》 (测试科学与仪器（英文版）)

年 卷 期：2010年第1卷第1期

页      面：41-45页

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by the Korea Research Foundation Grant funded by the Korean Government(MOEHRD),the MKE(The Ministry of Knowledge Economy,Korea) the ITRC(Information Technology Research Center)support program(NIPA-2009-(C1090-0902-0007)) 

主　　题：vehicle detection still image Adaboost local features 

摘      要：Vehicle detectition in still images is a comparatively difficult task. This paper presents a method for this task by using boosted local pattern detector constructed from two local features including Haar-like and oriented gradient features. The whole process is composed of three stages. In the first stage, local appearance features of vehicles and non-vehicle objects are extracted. Haar-tike and oriented gradient features are extracted separately in this stage as local features. In the second stage, Adabeost algorithm is used to select the most discriminative features as weak detectors from the two local feature sets, and a strong local pattern detector is built by the weighted combination of these selected weak detectors. Finally, vehicle detection can be performed in still images by using the boosted strong local feature detector. Experiment results show that the local pattern detector constructed in this way combines the advantages of Haar-like and oriented gradient features, and can achieve better detection results than the detector by using single Haar-like features.