Deep Discriminative Network with Inception Module for Person Re-identification

Convolutional neural networks have been verified to be exceptionally powerful on extracting semantic features, which contribute to a great progress in computer vision. However, focusing too much on the superiority, researchers seem to pay less attention to exploring CNNs' potential in other aspects, e.g. the ability to discriminate the difference. In this work we try to dig into the discriminative power of CNNs and introduce a deep discriminative network with inception module (DDN-IM) for person re-identification. Without individual feature extraction as prerequisite, input images from two different non-overlapping camera views are concatenated in depth at the beginning, followed by series of convolutional and nonlinear operations, etc. to predict their similarity. In addition, inception module is embedded in our network to boost the performance. We validate our proposal on several person re-identification datasets, CUHK01, QMUL GRID and PRID2011 included. We obtain competitive or superior performance compared to the state-of-the-art methods.