Deep Camera‐Aware Metric Learning for Person Reidentification
Wei Liu, Ping Liang, Lei Liu, Zhiqiang Hao, Xin Xu · Wireless Communications and Mobile Computing · 2021
Person reidentification (re‐id) suffers from a challenging issue due to the significant inconsistency of the camera network, including position, view, and brands. In this paper, we propose a deep camera‐aware metric learning (DCAML) model, where images on the identity‐level spaces are further projected into different camera‐level subspaces, which can explore the inherent relationship between identity and camera. Furthermore, we exploit dynamic training strategy to jointly multiple metrics for identity‐camera relationship learning and thus consumedly elevating the retrieval accuracy. Extensive experiments on the three public datasets demonstrated that our method performs competitive results compared to the state‐of‐the‐art person re‐id methods.