  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:
https://github.com/RenMin1991/cleaned-DukeMTMC-reID/
