论文标题
基于视频的人重新识别使用封闭的卷积复发神经网络
Video-based Person Re-Identification using Gated Convolutional Recurrent Neural Networks
论文作者
论文摘要
深层神经网络已成功地用于解决基于视频的人的重新识别问题，并报告了令人印象深刻的结果。现有的人重新ID网络旨在提取保留身份信息的区分功能。通常，整个视频框架被馈入神经网络，并且框架中的所有区域都得到了同样的处理。这可能是一个次优的选择，因为许多区域，例如视频中的背景区域都与该人无关。此外，感兴趣的人可能会被其他人或其他人遮住。这些无关的地区可能会阻碍人们的重新认同。在本文中，我们向深层神经网络介绍了一种新颖的门控机制。我们的门控机制将了解哪些区域有助于人重新识别，并让这些地区通过大门。无关的背景区域或阻塞区域被门滤出。在每个帧中，颜色通道和光流通道都提供了完全不同的信息。为了更好地利用此类信息，我们使用颜色通道和另一个门使用光流通道生成一个门。将这两个门组合在一起，以提供一种新型的融合方法提供更可靠的门。两个主要数据集的实验结果证明了由于提出的门控机制而导致的性能提高。
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.
