Abstract
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.
Highlights
Person re-identification is an extremely prominent technology in surveillance system due to its significance in pedestrian retrieval, such as criminal tracking, pedestrian locating
Directly introducing the soft-label method into target domain is not an effective way, because clustering based person re-id models have a conventional assumption that the number of pedestrian identities is acknowledged. This is an impractical assumption when we focus on target domain, and they can not estimate the cluster number to provide sufficient soft identity information. To make up these drawbacks analyzed above, we propose a novel multi-task transfer learning framework for cross domain person re-identification, which is learning discriminative feature representation from source domain, and transferring it into target domain with cluster estimating algorithm to support a soft multi-task learning procedure as well as in source domain, namely Deep Multi-task Transfer Network (DMTNet) method
RELATED WORK we review the research works on unsupervised person re-identification, which is divided as clustering based person re-id and cross domain person re-id methods according to surveys [9], [24]
Summary
Person re-identification (re-id) is an extremely prominent technology in surveillance system due to its significance in pedestrian retrieval, such as criminal tracking, pedestrian locating. The goal of person re-id is to identity the specific identity in gallery images, given a single probe image. This task is confronted with formidable challenges on account of severe variations in resolution, view-point, pose, occlusion and illumination across different cameras, on which most person re-id models focus to solve. Though existing person re-id approaches achieve an adequate performance, they need large amounts of annotations to train the models, which are under. That seriously confines the scalability of person re-id in realistic scene
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