Person re-identification (Re-ID) aims to match pedestrians under dis-joint
cameras. Most Re-ID methods formulate it as visual representation learning and
image search, and its accuracy is consequently affected greatly by the search
space. Spatial-temporal information has been proven to be efficient to filter
irrelevant negative samples and significantly improve Re-ID accuracy. However,
existing spatial-temporal person Re-ID methods are still rough and do not
exploit spatial-temporal information sufficiently. In this paper, we propose a
novel Instance-level and Spatial-Temporal Disentangled Re-ID method (InSTD), to
improve Re-ID accuracy. In our proposed framework, personalized information
such as moving direction is explicitly considered to further narrow down the
search space. Besides, the spatial-temporal transferring probability is
disentangled from joint distribution to marginal distribution, so that outliers
can also be well modeled. Abundant experimental analyses are presented, which
demonstrates the superiority and provides more insights into our method. The
proposed method achieves mAP of 90.8% on Market-1501 and 89.1% on
DukeMTMC-reID, improving from the baseline 82.2% and 72.7%, respectively.
Besides, in order to provide a better benchmark for person re-identification,
we release a cleaned data list of DukeMTMC-reID with this paper:
this https URL

本文提出了一种新的个性化、时空解耦的人员再识别方法（InSTD）, 其中加入了个性化信息用于狭窄化搜索空间并将空间 - 时间传递概率从联合分布解耦以适应离群值，实验结果表明，我们的方法在 Market-1501 和 DukeMTMC-reID 上的 mAP 分别提高了 8.6％和 16.4％。