摘要
传统的人物检测方法多是对于小样本,并且对于背景复杂的图片检测率很低,但是现实中的场景复杂,而且实时检测系统需要处理大量图片。针对传统检测方法在人体检测中的这些不足,提出了一种基于集成学习的方法——Gentleboost算法的人物检测方法,利用人物的身体碎片以及这些碎片相对于身体中心的相对位置作为特征,用Gentleboost算法训练的分类器来对人体进行分类。为了提高分类器的学习效率,解决复杂场景中人体检测的难题,提出了一种利用线性回归末端作为弱分类器的方法,从正、负两个方面对预测模型进行加权,改变了原来的仅从正预测进行加权的方法。将Gentleboost和基于YCbCr外表滤波加上身体部分特征的人物检测算法(简称为YCbCr算法)进行比较,并且对不同迭代次数的分类性能也进行了比较。实验结果表明,Gentle-boost的性能要优于YCbCr算法,而且随着迭代次数的增加,检测精度也随着增加,并且逐渐趋于稳定。该方法执行起来简单,数值上也比较稳定,正确率高,可以处理大量图片,解决了人体检测中的一些关键问题。
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 deteetion,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 deteeton problem of the image with complicate seenes,a approach which use the linear regression stump as the weak classifier is advanced.The approach weights to the pretietion model from the positive and negative classification,but not to weight it only from the positive aspect.This article compares Gentleboost algorithm with the deteeton 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 Gentle- boost is better than that of YCbCr algorithm and the method is simple to implement and the numberieal 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.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第23期187-190,共4页
Computer Engineering and Applications
基金
广西青年基金(No.桂科青0542035)
广西科学研究与技术开发计划项目(No.桂科攻0632007-1G)
