Posture-Guided and Multi-Granularity Feature Fusion for Person Reidentification

In the person reidentification system, the retrieved person image will have large posture differences, complex changes in perspectives, and misalignment of person images in the detection frame. In order to solve these problems, a reidentification algorithm is proposed, which can directly use the key point information of the human body for person image alignment and extract multi-granularity features based on this alignment. First, the posture prediction model is used to locate the key points of the human skeleton, and the person image is directly aligned according to the extracted skeleton key points, and then the multi-granularity features are extracted from the person image. The evaluation phase uses posture information combined with multi-granularity features for similarity matching. The experiment is carried out only using the identity(ID) loss function on the three public datasets of Market1501, CUHK03, and DukeMTMC-reID. The results show that the proposed algorithm has certain advantages.