TriHard Loss Based Multi-Task Person Re-identification

Aiming to enhance features discrimination for person re-identification, a deep multi-task person re-identification network based on TriHard loss is proposed. By learning identity and attributes labels simultaneously, the network can achieve more discriminative information of pedestrians. Firstly, the pre-trained ResNet-50 is loaded to extract pedestrian features of pre-processed images. Secondly, pedestrian features are fed into the designed multi-task network which consists of two branches. The two branches are trained jointly by minimizing combined TriHard loss of identity and attribute. Finally, the trained model is used to extract pedestrian appearances and attributes features. The features are used for person re-identification and attributes recognition. From the experimental results on the Market-1501 dataset and the DukeMTMC-reID dataset, it shows that features extracted from the proposed network are more discriminative. The multitask network achieves higher identification accuracy over the state-of-the-arts, together with the person attributes recognition.