Deep Camera-Aware Metric Learning for Person Reidentification

Wei Liu, Ping Liang, Lei Liu, Zhiqiang Hao, Xin Xu

Abstract: 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.