Correlation Enhancement with Graph Convolutional Network for Pedestrian Attribute Recognition

Pedestrian attribute recognition aims to predict human attributes in surveillance video. Since the correlations are different among attribute groups, mining the correlation variation between attributes is an effective way to improve the performance of pedestrian attribute recognition. In this paper, we propose a Feature Enhanced Multi-scale and Graph Convolutional Compound Network named FEM-GCNet to learn the enhanced correlations between attributes. The FEM-GCNet contains an enhanced feature extractor and a graph convolutional network. The enhanced feature extractor learns the enhanced features of attributes, improving the feature representation. The graph convolutional network exploits attribute correlation rules to mine the correlation features between attributes, which are embedded into the enhanced features to obtain the enhanced attribute correlations. Finally, a multi-label loss is used to train our model to identify pedestrian attributes. Experiments on RAPv1 and RAPv2 datasets demonstrate the robustness of the enhanced attribute correlations and the better performance of the FEM-GCNet.