Video-based Person Re-Identification using Gated Convolutional Recurrent Neural Networks
This work solves the problem of improving accuracy in video-based person re-identification for surveillance and security applications, representing an incremental advancement with a novel gating approach.
The paper tackles video-based person re-identification by addressing the issue of irrelevant background and occluded regions hindering performance, introducing a gating mechanism that filters out these areas and combining color and optical flow channels for improved reliability, resulting in demonstrated performance improvements on two major datasets.
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.