Deep Multi-Task Transfer Network for Cross Domain Person Re-Identification

As a prominent application of surveillance video analysis, person re-identification attracts much more research attention recently. Existing person re-identification models often focus on supervision by the pedestrian identity annotation, while it has limited scalability in realistic. Though several unsupervised person re-identification researches pay attention to solve this problem, they are either clustering based or cross domain based approaches, where a conventional assumption of them is the identity number of the target dataset is acknowledged. To relax this hypothesis, we propose a Deep Multi-task Transfer Network (DMTNet) for cross domain person re-identification, which conduct classification, attribute attention and identification task between source and target domains. There are three main novelties in DMTNet, including clustering number estimating algorithm to learn prior knowledge from source data to estimate the identity number, attribute attention importance learning rather than directly utilizing attribute information, and a multi-task transfer learning mechanism to transfer specific tasks cross domains. To prove the superiority of our DMTNet, we implement several compared experiments on DukeMTMC-reID and Market-1501 datasets, which results show the advancement of our network. Moreover, the discussions for different modules also point out the significance of the specific tasks.