Supervisor: Prof Tao Xiang
Person re-identification (Re-ID) is the problem of matching people across non-overlapping cameras views. Despite extensive efforts in the past decade, it remains an unsolved problem. This is because a person's appearance often changes dramatically across camera views due to changes in body pose, view angle, occlusion and illumination conditions. Recently, inspired by the success of deep neural networks, particularly deep Convoluational Neural Networks (CNNs) in various vision problems such as face verification, deep Re-ID models started to attract attention. However, unlike in other visual recognition problems, only modest success has been achieved. This is because Re-ID poses unique challenges to deep learning: apart from the difficulties in collecting large labelled datasets, there exist severe domain shift problems. Specifically, within each dataset, different camera views are drastically different causing cross-view domain shift; large cross-dataset domain shift also exists rendering the transfer learning between datasets difficult. In this project, we aim to develop novel deep neural network architectures and loss functions tailor made for the Re-ID problem, as well as novel deep transfer learning models to overcome the problem of lacking sufficient training data. Dr Tao Xiang has been a world-leading expert on person Re-ID (see http://www.eecs.qmul.ac.uk/~txiang/publications.html), and the research group has one of the most powerful GPU server clusters in the UK academia, thus providing a perfect environment for this project to succeed.