Person Re-identification on Mobile Devices Based on Deep Learning

Person Re-identification as an important supplement to face recognition, refers to a network of cameras with non-overlapping vision domains, through the use of computer vision technology to solve the cross-camera and cross-scene pedestrian recognition and retrieval. That is, determine whether there is a specific pedestrian to be detected in the different images or different video sequences. At present, many person re-identification research works mainly carried out through experimental verification and evaluation on large ReID datasets such as Market-1501, DukeMTMC-reID, MSMT17, and CUHK03. In this paper, based on the existing deep-learning person re-identification research, combines with the actual application scenarios, under the premise of analyzing the technical feasibility, we propose a complete process for Person Re-identification based on mobile devices, aims to combine pedestrian detection and person re-identification to perform real-time pedestrian detection and query. In this process, not only the features extracted by pedestrians can be reused, but also the research on person re-identification can be better and effectively applied, such as tracking criminals and searching for missing children.