The person re-identification is one of the most significant problems in computer vision and surveillance systems. The recent success of deep convolutional neural networks in image classification has inspired researchers to investigate the application of deep learning to the person re-identification. However, the huge amount of research on this problem considers classical settings, where pedestrians are captured by static surveillance cameras, although there is a growing demand for analyzing images and videos taken by drones. In this paper, we aim at filling this gap and provide insights on the person re-identification from drones. To our knowledge, it is the first attempt to tackle this problem under such constraints. We present the person re-identification dataset, named DRone HIT (DRHIT01), which is collected by using a drone. It contains 101 unique pedestrians, which are annotated with their identities. Each pedestrian has about 500 images. We propose to use a combination of triplet and large-margin Gaussian mixture (L-GM) loss to tackle the drone-based person re-identification problem. The proposed network equipped with multi-branch design, channel group learning, and combination of loss functions is evaluated on the DRHIT01 dataset. Besides, transfer learning from the most popular person re-identification datasets is evaluated. Experiment results demonstrate the importance of transfer learning and show that the proposed model outperforms the classic deep learning approach.

人员重新识别是计算机视觉和监视系统中最重要的问题之一。深度卷积神经网络在图像分类中的最新成功激发了研究人员研究深度学习在人重新识别中的应用。然而，尽管对分析无人机拍摄的图像和视频的需求不断增长，但对这一问题的大量研究还是在经典环境中进行的，在这些环境中，行人被静态监控摄像头捕获。在本文中，我们旨在填补这一空白，并提供有关无人机重新识别人员的见解。据我们所知，这是在这种限制下解决这个问题的第一次尝试。我们提供人员重新识别数据集，称为DR一种HIT（DRHIT01），它是使用无人机收集的。它包含101位独特的行人，并带有其身份注释。每个行人大约有500张图像。我们建议结合使用三重态和大幅度高斯混合（L-GM）损失来解决基于无人机的人员重新识别问题。在DRHIT01数据集上评估了拟议的网络，该网络具有多分支设计，信道组学习和损失函数的组合。此外，还将评估从最热门人物重新识别数据集中获得的转移学习。实验结果证明了转移学习的重要性，并表明该模型优于经典的深度学习方法。