关键词: 人物检测, 特征提取, 分类器, Gentleboost算法

Abstract: The traditional person detection method is more about small number of samples.Using this method，the detection rate of the image with complex scenes is low.But the reality scene is complex and the real-time detection system need to handle a large number of images.In order to remedy the deficiency of the traditional test methods in the human detection，a person detection approach based on the integrated study—Gentleboost algorithm is suggested.The method uses human patch and its relative position with the person’s center as the feature and uses Gentleboost algorithm train the classifier which is used to classify the person.In order to improve the learning efficiency of the classifier and solve the detecton problem of the image with complicate scenes，a approach which use the linear regression stump as the weak classifier is advanced.The approach weights to the pretiction model from the positive and negative classification，but not to weight it only from the positive aspect.This article compares Gentleboost algorithm with the detecton method based on YCbCr surface filter and the body part feature（for short YCbCr algorithm）and also compares classification with different iteration.The result of the experiment shows that the classification of Gentleboost is better than that of YCbCr algorithm and the method is simple to implement and the numberical is also stable and the positive detection rate is high.This method can handle a large number of images and solve some key issues of human detection.

Key words: people detection, features extraction, classifier, Gentleboost algorithm