关键词: 行人检测, 支持向量机（SVM）, EasyEnsemble SVM分类器, 聚合支持向量机（Ensemble SVM）

Abstract: Since the accuracy is low due to the imbalance of positive and negative pedestrians in SVM pedestrian detection method which adopt under-sampling method, this paper proposes an EnsembleSVM pedestrian detection method by combining under-sampling and EasyEnsemble. Firstly, selecting negative sample as initial training sample randomly that is divided into multiple sub-negative sample sets equalizing the positive sample set, building balanced sub-training sets and linear assembling EasyEnsemble SVM. Then negative sample is classified and judged by using EasyEnsemble SVM, making misjudged sample as hard sample, building balanced sub-training sets again and training sub-classifier, combining which with EasyEnsemble SVM to get the Ensemble SVM classifier. Experiments on INRIA pedestrian data show the approach is better than classical SVM pedestrian detection algorithm in both detection speed and accuracy rate.

Key words: pedestrian detection, Support Vector Machine（SVM）, EasyEnsemble SVM classifier, Ensemble SVM