  Deep neural networks have been successfully applied to solving the
video-based person re-identification problem with impressive results reported.
The existing networks for person re-id are designed to extract discriminative
features that preserve the identity information. Usually, whole video frames
are fed into the neural networks and all the regions in a frame are equally
treated. This may be a suboptimal choice because many regions, e.g., background
regions in the video, are not related to the person. Furthermore, the person of
interest may be occluded by another person or something else. These unrelated
regions may hinder person re-identification. In this paper, we introduce a
novel gating mechanism to deep neural networks. Our gating mechanism will learn
which regions are helpful for person re-identification and let these regions
pass the gate. The unrelated background regions or occluding regions are
filtered out by the gate. In each frame, the color channels and optical flow
channels provide quite different information. To better leverage such
information, we generate one gate using the color channels and another gate
using the optical flow channels. These two gates are combined to provide a more
reliable gate with a novel fusion method. Experimental results on two major
datasets demonstrate the performance improvements due to the proposed gating
mechanism.
