Person re-identification (ReID) focuses on identifying people across
different scenes in video surveillance, which is usually formulated as a binary
classification task or a ranking task in current person ReID approaches. In
this paper, we take both tasks into account and propose a multi-task deep
network (MTDnet) that makes use of their own advantages and jointly optimize
the two tasks simultaneously for person ReID. To the best of our knowledge, we
are the first to integrate both tasks in one network to solve the person ReID.
We show that our proposed architecture significantly boosts the performance.
Furthermore, deep architecture in general requires a sufficient dataset for
training, which is usually not met in person ReID. 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 outperforms most of existing person ReID
algorithms on representative datasets including CUHK03, CUHK01, VIPeR, iLIDS
and PRID2011, which clearly demonstrates the effectiveness of the proposed
approach.

本文提出了一种多任务深度网络并联考虑二分类和排名任务，结合交叉域架构训练以解决人员再识别问题，实验结果表明，该方法有效性显著优于现有算法。