A Multi-task Deep Network for Person Re-identification

Person re-identification (RID) focuses on identifying people across different scenes in video surveillance, which is usually formulated as either a binary classification task or a ranking task in current person RID approaches. To the best of our knowledge, none of existing work treats the two tasks simultaneously. In this paper, we take both tasks into account and propose a multi-task deep network (MTDnet) to jointly optimize the two tasks simultaneously for person RID. We show that our proposed architecture significantly boosts the performance. Furthermore, a good performance of any deep architectures requires a sufficient training set which is usually not met in person RID. To cope with this situation, we further extend the MTDnet and propose a cross-domain architecture that is capable of using an auxiliary set to assist training on small target sets. In the experiments, our approach significantly outperforms previous state-of-the-art methods on almost all the datasets, which clearly demonstrates the effectiveness of the proposed approach.