• convolutional network
• deep learning
• Person re-identification
• siamese network

In this thesis work, a person re-identification tool is presented.
The person re-identification problem is defined as the process that recognizes if a person has been observed in different locations over a set of non-overlapping camera views.
The problem, presents various challenges due to low image quality, different pose, different illumination that can be affect the recognizing process.
The system presented is based on an existing deep convolutional network architecture specifically designed to address the problem of re-identification.
The network is able to learn visual features and a corresponding similarity metric for person re-identification. Given a pair of images as input, the network computes the similarity score to indicate if the two images depict the same person.
The thesis describes the entire network implementation process focusing on the implementation of the cross-input neighborhood differences layer, (core component of the network) able to capture local relationships between the two input images based on mid- level features from each input image.
Experiments have been performed on the public person re-identification dataset CUHK03, in order to compare the results obtained, with the results of networks that represents the state of art. Specifically, the results of the network reproduced have been compared with the results of the original network and the results of a novel network based on the GoogleNet, which actually significantly outperforms the state of art.
We show how the results of the system implemented are comparable or slightly higher than the original one and significantly lower with respect to the novel network.
A downside of the latter network, however, is in its inefficiency in terms of computation time of the similarity between two images. This is an aspect that cannot underestimated in real time applications such as person re-identification.