A Visible-Infrared person re-identification algorithm based on skeleton Insight Criss-Cross network

There are significant inter-class differences in the cross-modal feature space. If the pedestrian skeleton information is used as the discrimination basis for cross-modal person re-identification, the problem of mismatch between the skeleton features and the ID attributes is inevitable. In order to solve the above problems, this paper proposes a novel Skeleton Insight Criss-Cross Network (SI-CCN), which consists of a Skeleton Insight Module (SIM) and a Criss-Cross Module (CCM). The former uses the skeleton hierarchical mechanism to extract the key skeleton information of the pedestrian limb area, obtain the characteristics of the skeleton key points at the pixel level, and the skeleton key points are used as the graph nodes to construct the skeleton posture structure of the pedestrian. And as a result, the SIM module can not only accurately capture the spatial information of various parts of the pedestrian, but also maintain the relative positional relationship between the key points of the skeleton to form a complete skeleton structure. The latter cooperatively optimizes the characteristics of high-dimensional skeleton and low-dimensional identity identification by using a cross-learning mechanism. In order to effectively capture the diverse skeleton posture, the attention distribution of the two in the feature extraction process is dynamically adjusted to integrate identity details at the same time, and the consistency of cross-modal features is improved. The experiments on the two cross-modal person re-identification data sets of SYSU-MM01 and RegDB show that the Rank-1 and mAP of the SI-CCN on the SYSU-MM01 data set are 81.94% and 76.92%, respectively, and the Rank-1 and mAP on the RegDB data set are 95.49% and 95.67%, respectively. The proposed method has better performance than that of the recent representative methods.