Identity-seeking self-supervised representation learning for a smart person-tracking system
Person re-identification(Person ReID)technology is crucial for applications such as security monitoring and individual tracking.However,the scarcity and high cost of annotated data limit its widespread use.To address this issue,a person search system based on Identity-seeking Self-supervised Representation(ISR)learning method is designed to learn and extract feature representations of individuals from large-scale unlabeled video data.The system architecture consists of three modules:First,the YOLOV8 model is used to detect individuals in video streams and automatically crop gallery ima-ges;Second,the ISR learning method is employed to extract features from gallery images and construct a feature database;Finally,the system retrieves gallery images similar to the query image from the feature database and associates them with corresponding video frames.Experimental results demonstrate that the system is accurate and efficient in search,with broad application value and practical potential.
person re-identificationidentity-seeking self-supervised representation learningunlabeled data learning
NETL
NSTL
万方数据
