Vehicle Detection in Still Images by Using Boosted Local Feature Detector

摘要

Vehicle detection 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-like and oriented gradient features are extracted separately in this stage as local features.In the second stage,Adaboost 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.