论文标题
不是3D重新ID：一个简单的单流2D卷积，用于稳健的视频重新识别
Not 3D Re-ID: a Simple Single Stream 2D Convolution for Robust Video Re-identification
论文作者
论文摘要
基于视频的人的重新识别最近受到了越来越多的关注，因为它在监视视频分析中起着重要作用。基于视频的重新ID是通过每个人通过多个图像帧从视频中学习功能来扩展基于图像的重新识别方法。大多数当代视频重新ID方法都使用3D卷积或多支气网络利用复杂的基于CNNB的网络体系结构来提取时空视频功能。相比之下，在本文中，我们说明了一个简单的单流2D卷积网络的出色性能，该网络利用Resnet50-IBN体系结构来提取框架级别的功能，然后对剪辑级别的特征进行时间关注。这些剪辑级功能可以推广以通过平均而没有任何额外的费用来提取视频级别的功能。我们的方法使用最佳视频重新练习和在数据集之间的转移学习，以优于MARS，PRID2011和ILIDS-VID数据集的现有最新方法，分别为89：62％，97：75％，97：33％：33％：33％的准确性，以及对MARS的84：61％的构图，以及在84：61％的构图中，以及建立了复杂性的3D型号。当代工作。相反，我们的工作表明，由2D卷积网络提取的全球功能足以表现出可靠的视频重新ID状态。
Video-based person re-identification has received increasing attention recently, as it plays an important role within surveillance video analysis. Video-based Re-ID is an expansion of earlier image-based re-identification methods by learning features from a video via multiple image frames for each person. Most contemporary video Re-ID methods utilise complex CNNbased network architectures using 3D convolution or multibranch networks to extract spatial-temporal video features. By contrast, in this paper, we illustrate superior performance from a simple single stream 2D convolution network leveraging the ResNet50-IBN architecture to extract frame-level features followed by temporal attention for clip level features. These clip level features can be generalised to extract video level features by averaging without any significant additional cost. Our approach uses best video Re-ID practice and transfer learning between datasets to outperform existing state-of-the-art approaches on the MARS, PRID2011 and iLIDS-VID datasets with 89:62%, 97:75%, 97:33% rank-1 accuracy respectively and with 84:61% mAP for MARS, without reliance on complex and memory intensive 3D convolutions or multi-stream networks architectures as found in other contemporary work. Conversely, our work shows that global features extracted by the 2D convolution network are a sufficient representation for robust state of the art video Re-ID.
