Person Re-identification on Mobile Devices Based on Deep Learning
Person re-identification, as an important supplement to face recognition, it can solve the cross-camera and cross-scene pedestrian recognition and retrieval, determining whether there is a specific pedestrian to be detected in the image or video sequence. At present, many person re-identification researches are mainly carried out through experimental verification and evaluation on large person re-identification datasets such as Market-1501, DukeMTMC-reID, MSMT17, and CUHK03. In this paper, based on the existing deep learning person re-identification research and combining with the actual application scenarios on the premise of analyzing the technical feasibility, we propose a complete process for person re-identification with mobile devices, which aims to combine pedestrian detection with 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 applied effectively, such as tracking criminals and searching for missing children.
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