Multi-Level Feature Network With Multi-Loss for Person Re-Identification

Person re-identification has become a challenging task due to various factors. One key to effective person re-identification is the extraction of the discriminative features of a person's appearance. Most previous works based on deep learning extract pedestrian characteristics from neural networks but only from the top feature layer. However, the low-layer feature could be more discriminative in certain circumstances. Hence, we propose a method, named the multi-level feature network with multiple losses (MFML), which has a multi-branch network architecture that consists of multiple middle layers and one top layer for feature representations. To extract the discriminative middle-layer features and have a good effect on deeper layers, we utilize the triplet loss function to train the middle-layer features. For the top layer, we focus on learning more discriminative feature representations, so we utilize the hybrid loss (HL) function to train the top-layer feature. Instead of concatenating multilayer features directly, we concatenate the weighted middle-layer features and the weighted top-layer feature as the discriminative features in the testing phase. The extensive evaluations conducted on three datasets show that our method achieves a competitive accuracy level compared with the state-of-the-art methods.