Most of current person re-identification (ReID) methods neglect a
spatial-temporal constraint. Given a query image, conventional methods compute
the feature distances between the query image and all the gallery images and
return a similarity ranked table. When the gallery database is very large in
practice, these approaches fail to obtain a good performance due to appearance
ambiguity across different camera views. In this paper, we propose a novel
two-stream spatial-temporal person ReID (st-ReID) framework that mines both
visual semantic information and spatial-temporal information. To this end, a
joint similarity metric with Logistic Smoothing (LS) is introduced to integrate
two kinds of heterogeneous information into a unified framework. To approximate
a complex spatial-temporal probability distribution, we develop a fast
Histogram-Parzen (HP) method. With the help of the spatial-temporal constraint,
the st-ReID model eliminates lots of irrelevant images and thus narrows the
gallery database. Without bells and whistles, our st-ReID method achieves
rank-1 accuracy of 98.1\% on Market-1501 and 94.4\% on DukeMTMC-reID, improving
from the baselines 91.2\% and 83.8\%, respectively, outperforming all previous
state-of-the-art methods by a large margin.

本研究提出了一种新的两流空间 - 时间人物重新识别（st-ReID）框架，该框架挖掘了视觉语义信息和空间 - 时间信息，引入 Logistic Smoothing（LS）联合相似性度量，使用快速直方图 - Parzen（HP）方法近似复杂的空间 - 时间概率分布，消除了很多无关图像，在 Market-1501 和 DukeMTMC-reID 数据集上均取得了最好的准确性，进步幅度达到了先前方法的很大程度。