东南大学学报（自然科学版）2017，Vol.47Issue(4)：679-684,6.DOI:10.3969/j.issn.1001-0505.2017.04.009
基于深度模型的场景自适应行人检测
Scene adaptive pedestrian detection algorithm based on deep model
摘要
Abstract
To solve the problem that the detection effect of the existing machine learning based pedestrian detection algorithms decreases dramatically when the distributions of training samples and scene target samples do not match, a scene adaptive pedestrian detection algorithm based on the deep model is proposed.First, inspired by the Bagging(Bootstrap aggregating) mechanism, multiple relatively independent source samples are used to build multiple classifiers and then target training samples with confidence score are generated by voting.Secondly, using the automatic feature extraction ability of DCNN (deep convolutional neural network) and adding a deep auto-encoder to perform the source-target scene feature similarity calculation, a deep model-based scene adaptive classifier model is proposed and its training algorithm is designed.The experiments on the KITTI dataset demonstrate that the proposed algorithm performs better than the existing non-scene adaptive pedestrian detection algorithms.Besides, compared with the existing scene adaptive object detection algorithms, the proposed algorithm improves the detection rate on average by approximately 4%.关键词
场景自适应/行人检测/深度结构/卷积神经网络Key words
scene adaption/pedestrian detection/deep structure/deep convolutional neural network分类
信息技术与安全科学引用本文复制引用
蔡英凤,王海,孙晓强,袁朝春,陈龙,江浩斌..基于深度模型的场景自适应行人检测[J].东南大学学报（自然科学版）,2017,47(4):679-684,6.基金项目
国家自然科学基金资助项目(U1564201,61403172,61601203)、中国博士后基金资助项目(2014M561592,2015T80511)、江苏省重点研发计划资助项目(BE2016149)、江苏省自然科学基金资助项目(BK20140555)、江苏省六大人才高峰资助项目(2014-DZXX-040,2015-JXQC-012). （U1564201,61403172,61601203）
