Pedestrian attribute recognition based on attribute correlation

Pedestrian attribute recognition is widely used in pedestrian tracking and pedestrian re-identification. This task confronts two fundamental challenges. One comes from its multi-label nature; the other one comes from the characteristics of data samples, such as the class imbalance and the partial occlusion. In this work, we propose a Cross Attribute and Feature Network (CAFN) that fully exploits the correlations between any pair of attributes for the pedestrian attribute recognition to tackle these challenges. Concretely, CAFN contains two modules: Cross-attribute Attention Module (C2AM) and Cross-feature Attention Module (CFAM). C2AM enables the network to automatically learn the relation matrix during the training process which can quantify the correlations between any pair of attributes in the attribute set, and CFAM is introduced to fuse different attribute features to generate more accurate and robust attribute features. Extensive experiments demonstrate that the proposed CAFN performs favorably compared with state-of-the-art approaches.