Jointly learning deep features, deformable parts, occlusion and classification for pedestrian detection
Publication in refereed journal

替代計量分析
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其它資訊
摘要Feature extraction, deformation handling, occlusion handling, and classification are four important components in pedestrian detection. Existing methods learn or design these components either individually or sequentially. The interaction among these components is not yet well explored. This paper proposes that they should be jointly learned in order to maximize their strengths through cooperation. We formulate these four components into a joint deep learning framework and propose a new deep network architecture (Code available on www.ee.cuhk.edu.hk/~wlouyang/projects/ouyangWiccv13Joint/index.html). By establishing automatic, mutual interaction among components, the deep model has average miss rate 8.57%/11.71%on the Caltech benchmark dataset with new/original annotations.
出版社接受日期11.08.2017
著者Wanli Ouyang, Hui Zhou, Hongsheng Li, Quanquan Li, Junjie Yan, Xiaogang Wang
期刊名稱IEEE Transactions on Pattern Analysis and Machine Intelligence
出版年份2018
月份8
卷號40
期次8
出版社IEEE
頁次1874 - 1887
國際標準期刊號0162-8828
電子國際標準期刊號1939-3539
語言美式英語

上次更新時間 2021-09-01 於 01:13