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Deep neural networks have been successfully applied to solving thevideo-based person re-identification problem with impressive results reported.The existing networks for person re-id are designed to extract discriminativefeatures that preserve the identity information. Usually, whole video framesare fed into the neural networks and all the regions in a frame are equallytreated. This may be a suboptimal choice because many regions, e.g., backgroundregions in the video, are not related to the person. Furthermore, the person ofinterest may be occluded by another person or something else. These unrelatedregions may hinder person re-identification. In this paper, we introduce anovel gating mechanism to deep neural networks. Our gating mechanism will learnwhich regions are helpful for person re-identification and let these regionspass the gate. The unrelated background regions or occluding regions arefiltered out by the gate. In each frame, the color channels and optical flowchannels provide quite different information. To better leverage suchinformation, we generate one gate using the color channels and another gateusing the optical flow channels. These two gates are combined to provide a morereliable gate with a novel fusion method. Experimental results on two majordatasets demonstrate the performance improvements due to the proposed gatingmechanism.

Video-based Person Re-Identification using Gated Convolutional Recurrent Neural Networks