Unified Framework for Joint Attribute Classification and Person Re-identification

Person re-identification (re-id) is an essential task in video surveillance. Existing approaches mainly concentrate on extracting useful appearance features from deep convolutional neural networks. However, they don't utilize or only partially utilize semantic information such as attributes or person orientation. In this paper, we propose a novel deep neural network framework that greatly improves the accuracy of person re-id and also that of attribute classification. The proposed framework includes two branches, the identity one and the attribute one. The identity branch employs the refined triplet loss and exploits local cues from different regions of the pedestrian body. The attribute branch has an effective attribute predictor containing hierarchical attribute loss functions. After training the identification and attribute classifications, pedestrian representations are derived which contains hierarchical attribute information. The experimental results on DukeMTMC-reID and Matket-1501 datasets validate the effectiveness of the proposed framework in both person re-id and attribute classification. For person re-id, the Rank-1 accuracy is improved by 7.99% and 2.76%, and the mAP is improved by 14.72% and 5.45% on DukeMTMC-reID and Market-1501 datasets respectively. Specifically, it yields 90.95% in accuracy of attribute classification on DukeMTMC-reID, which outperforms the state-of-the-art attribute classification methods by 3.42%.