People Re-Identification by Multi-Branch CNN with Multi-Scale Features

People re-identification is a retrieval problem to find a person of interest among a gallery of person images from different cameras in various poses or view angles. How to get a strong feature representation for a person image plays an important role in performing people re-identification. In this paper, we present a novel end-to-end framework that extracts both global and local features with multiple scales to generate more discriminative representations. The model we design is a multi-branch network consisting of one global branch to obtain features of the whole input image from different convolutional layers and several local branches to obtain features from horizontal partitions in different granularities. Our method achieves state-of-the-art results on three challenging datasets (Market-1501, CUHK03 and DukeMTMC-reid).