Person re-identification(Re-ID)is the scientific task of finding specific person images of a person in a non-overlapping camera networks,and has achieved many breakthroughs recently.However,it remains very challenging...Person re-identification(Re-ID)is the scientific task of finding specific person images of a person in a non-overlapping camera networks,and has achieved many breakthroughs recently.However,it remains very challenging in adverse environmental conditions,especially in dark areas or at nighttime due to the imaging limitations of a single visible light source.To handle this problem,we propose a novel deep red green blue(RGB)-thermal(RGBT)representation learning framework for a single modality RGB person ReID.Due to the lack of thermal data in prevalent RGB Re-ID datasets,we propose to use the generative adversarial network to translate labeled RGB images of person to thermal infrared ones,trained on existing RGBT datasets.The labeled RGB images and the synthetic thermal images make up a labeled RGBT training set,and we propose a cross-modal attention network to learn effective RGBT representations for person Re-ID in day and night by leveraging the complementary advantages of RGB and thermal modalities.Extensive experiments on Market1501,CUHK03 and Duke MTMC-re ID datasets demonstrate the effectiveness of our method,which achieves stateof-the-art performance on all above person Re-ID datasets.展开更多
基金supported by National Natural Science Foundation of China(Nos.61976002,61976003 and 61860206004)Natural Science Foundation of Anhui Higher Education Institutions of China(No.KJ2019A0033)the Open Project Program of the National Laboratory of Pattern Recognition(No.201900046)。
文摘Person re-identification(Re-ID)is the scientific task of finding specific person images of a person in a non-overlapping camera networks,and has achieved many breakthroughs recently.However,it remains very challenging in adverse environmental conditions,especially in dark areas or at nighttime due to the imaging limitations of a single visible light source.To handle this problem,we propose a novel deep red green blue(RGB)-thermal(RGBT)representation learning framework for a single modality RGB person ReID.Due to the lack of thermal data in prevalent RGB Re-ID datasets,we propose to use the generative adversarial network to translate labeled RGB images of person to thermal infrared ones,trained on existing RGBT datasets.The labeled RGB images and the synthetic thermal images make up a labeled RGBT training set,and we propose a cross-modal attention network to learn effective RGBT representations for person Re-ID in day and night by leveraging the complementary advantages of RGB and thermal modalities.Extensive experiments on Market1501,CUHK03 and Duke MTMC-re ID datasets demonstrate the effectiveness of our method,which achieves stateof-the-art performance on all above person Re-ID datasets.
