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.

针对数据不平衡和类内差异，提出了一种使用级联的复杂对等AdaBoost分类器的行人检测方法。贪婪地学习了一系列AdaBoost分类器，以及负面的示例挖掘。级联中分类器的复杂性不受限制，因此使用了更多负面示例进行训练。此外，级联成为强大的对等分类器的集合，可处理类内变异。为了以较高的检测率对AdaBoost分类器进行本地训练，使用了一种优化策略来丢弃最难的负面训练示例，而不是降低其阈值。使用聚合渠道功能（ACF），该方法在Caltech行人基准和Inria行人数据集上分别达到35％和14％的未命中率，这比日益复杂的AdaBoost分类器要低，分别为44％和17％。利用区域提案网络（RPN）提取的深层特征，该方法在加州理工学院行人基准上的未命中率达到了10.06％，也低于日益复杂的级联中的10.53％。这项研究表明，该方法可以使用更多的负面例子来训练行人检测器。它优于现有的日益复杂的分类器级联。这项研究表明，该方法可以使用更多的负面实例来训练行人检测器。它优于现有的日益复杂的分类器级联。这项研究表明，该方法可以使用更多的负面实例来训练行人检测器。它优于现有的日益复杂的分类器级联。