Improved pedestrian detection with peer AdaBoost cascade  

基于朋辈AdaBoost分类器级联的行人检测

作　　者：FU Hong-pu ZOU Bei-ji ZHU Cheng-zhang DAI Yu-lan JIANG Ling-zi CHANG Zhe 傅红普;邹北骥;朱承璋;戴玉兰;姜灵子;昌喆(School of Computer Science and Engineering,Central South University,Changsha 410083,China;School of Information Science and Engineering,Hunan First Normal University,Changsha 410205,China;Hunan Province Engineering Technology Research Center of Computer Vision and Intelligent Medical Treatment,Changsha 410083,China;School of Literature and Journalism,Central South University,Changsha 410083,China;Mobile Health Ministry of Education-China Mobile Joint Laboratory,Changsha 410083,China)

机构地区：[1]School of Computer Science and Engineering,Central South University,Changsha 410083,China [2]School of Information Science and Engineering,Hunan First Normal University,Changsha 410205,China [3]Hunan Province Engineering Technology Research Center of Computer Vision and Intelligent Medical Treatment,Changsha 410083,China [4]School of Literature and Journalism,Central South University,Changsha 410083,China [5]Mobile Health Ministry of Education-China Mobile Joint Laboratory,Changsha 410083,China©Central South University Press and Springer-Verlag GmbH Germany,part of Springer Nature 2020

出　　处：《Journal of Central South University》2020年第8期2269-2279,共11页中南大学学报（英文版）

基　　金：Project(2018AAA0102102)supported by the National Science and Technology Major Project,China;Project(2017WK2074)supported by the Planned Science and Technology Project of Hunan Province,China;Project(B18059)supported by the National 111 Project,China;Project(61702559)supported by the National Natural Science Foundation of China。

摘　　要：Focusing on data imbalance and intraclass variation,an improved pedestrian detection with a cascade of complex peer AdaBoost classifiers is proposed.The series of the AdaBoost classifiers are learned greedily,along with negative example mining.The complexity of classifiers in the cascade is not limited,so more negative examples are used for training.Furthermore,the cascade becomes an ensemble of strong peer classifiers,which treats intraclass variation.To locally train the AdaBoost classifiers with a high detection rate,a refining strategy is used to discard the hardest negative training examples rather than decreasing their thresholds.Using the aggregate channel feature(ACF),the method achieves miss rates of 35%and 14%on the Caltech pedestrian benchmark and Inria pedestrian dataset,respectively,which are lower than that of increasingly complex AdaBoost classifiers,i.e.,44%and 17%,respectively.Using deep features extracted by the region proposal network(RPN),the method achieves a miss rate of 10.06%on the Caltech pedestrian benchmark,which is also lower than 10.53%from the increasingly complex cascade.This study shows that the proposed method can use more negative examples to train the pedestrian detector.It outperforms the existing cascade of increasingly complex classifiers.针对训练数据不平衡和类内差异,本文提出了使用等同复杂度AdaBoost分类器的级联来检测行人,称为朋辈级联。利用难负样本挖掘操作,贪婪训练一系列的AdaBoost阶段分类器。朋辈级联不限制分类器的复杂度,从而得以利用更多负训练样本。并且,本文级联成为了强朋辈分类器的集成,从而能在一定程度上应对行人的类内差异。为就地训练出高检测率的AdaBoost分类器,提出提纯操作来丢弃一些难负样本。提纯操作替代以往直接降低分类器阈值的操作,保留了每个分类器的训练优化性能。实验结果表明,在Inria和Caltech pedestrian benchmark两个公开行人数据集,使用聚合通道特征(ACF)朋辈级联的检测性能比现有逐渐复杂分类器级联的检测性能好很多。使用RPN提取的深度学习特征时,朋辈级联的性能明显更好。

关 键 词：peer classifier hard negative refining pedestrian detection CASCADE 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]