Person re-identification is to search for a correct match for a person of interest across different camera views among a large number of impostors. While the approaches based on RGB modality have been widely studied to re-identify people, other modalities could be exploited as additional information sources, like depth and skeleton modalities. In this paper, we perform multi- and single-modal person re-identification using RGB, depth and skeleton modalities obtained by RGB-D sensors. First of all, the depth and RGB images are divided into three regions of head, torso and legs. Then, each region is explained by the histograms of local vector patterns (LVP). In depth and RGB modalities, the depth values of pixels and the gray levels of pixels are used for extracting LVPs, respectively. The skeleton features are extracted by computing the various Euclidean distances for the joint points of skeleton images. Then, features extracted by different modalities are combined as double and triple combinations using score-level fusion. The experiments are evaluated on two RGBD-ID and KinectREID databases, and results illustrate the acceptable performance of the proposed method.

中文翻译：

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使用 RGB-D 传感器的 RGB、深度和骨架信息的短期行人重新识别

人员重新识别是在大量冒名顶替者中跨不同摄像机视图搜索感兴趣的人的正确匹配。虽然基于 RGB 模态的方法已被广泛研究以重新识别人，但可以利用其他模态作为附加信息源，如深度和骨架模态。在本文中，我们使用由 RGB-D 传感器获得的 RGB、深度和骨架模态进行多模态和单模态行人重新识别。首先将深度和RGB图像分为头部、躯干和腿部三个区域。然后，每个区域由局部向量模式 (LVP) 的直方图解释。在深度和 RGB 模式中，像素的深度值和像素的灰度分别用于提取 LVP。骨骼特征是通过计算骨骼图像连接点的各种欧氏距离来提取的。然后，使用分数级融合将不同模态提取的特征组合为双重和三重组合。实验在两个 RGBD-ID 和 KinectREID 数据库上进行了评估，结果说明了所提出方法的可接受的性能。