Pedestrian Detection Aided by Deep Learning Attributes Task

摘要：

　　Deep Learning methods have achieved great successes in pedestrian detection owing to their ability of learning discriminative features from pixel level.However,most of the popular methods only consider using the deep structure as a single feature extractor(one attribute)which may confuse positive with hard negative samples.To address this ambiguity,this work jointly learns three different attributes,including parts,deformation and similarity attributes.This paper proposes a new deep network which jointly optimizes the three attributes and formulates them to form a binary classification task.Extensive experiments show that the proposed method outperforms competing methods on the challenging Caltech and ETH benchmarks.

关键词： Deep learning Similarity attributes Pedestrian detection

作者: Chao Qiu Yinhui Zhang Jieqiong Wang Zifen He

作者单位: Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650500,China

会议类型: 国际会议

会议名称: 第七届全国模式识别学术会议(The 7th Chinese Conference on Pattern Recognition,CCPR2016)

会议地点: 成都

会议语种:英文

页码: 201-210

在线出版日期: 2016-11-03（万方平台首次上网日期，不代表论文的发表时间）