  Person re-identification across disjoint camera views has been widely applied
in video surveillance yet it is still a challenging problem. One of the major
challenges lies in the lack of spatial and temporal cues, which makes it
difficult to deal with large variations of lighting conditions, viewing angles,
body poses and occlusions. Recently, several deep learning based person
re-identification approaches have been proposed and achieved remarkable
performance. However, most of those approaches extract discriminative features
from the whole frame at one glimpse without differentiating various parts of
the persons to identify. It is essentially important to examine multiple highly
discriminative local regions of the person images in details through multiple
glimpses for dealing with the large appearance variance. In this paper, we
propose a new soft attention based model, i.e., the end to-end Comparative
Attention Network (CAN), specifically tailored for the task of person
re-identification. The end-to-end CAN learns to selectively focus on parts of
pairs of person images after taking a few glimpses of them and adaptively
comparing their appearance. The CAN model is able to learn which parts of
images are relevant for discerning persons and automatically integrates
information from different parts to determine whether a pair of images belongs
to the same person. In other words, our proposed CAN model simulates the human
perception process to verify whether two images are from the same person.
Extensive experiments on three benchmark person re-identification datasets,
including CUHK01, CHUHK03 and Market-1501, clearly demonstrate that our
proposed end-to-end CAN for person re-identification outperforms well
established baselines significantly and offer new state-of-the-art performance.
