Improved pedestrian detection with peer AdaBoost cascade

Abstract

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.

摘要

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。