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首页> 外文期刊>Iranian Journal of Science and Technology, Transactions of Electrical Engineering >Short-Term Person Re-identification Using RGB, Depth and Skeleton Information of RGB-D Sensors
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Short-Term Person Re-identification Using RGB, Depth and Skeleton Information of RGB-D Sensors

机译：使用RGB-D传感器的RGB，深度和骨架信息进行短期人员重新识别

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摘要

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.
机译：人员重新识别是要在大量冒名顶替者之间的不同摄像机视角中寻找感兴趣的人的正确匹配项。尽管已经广泛研究了基于RGB模态的方法来重新识别人，但是其他模态也可以用作其他信息源，例如深度和骨架模态。在本文中，我们使用RGB-D传感器获得的RGB，深度和骨架模态执行多模态和单模态人的重新识别。首先，深度和RGB图像分为头部，躯干和腿部三个区域。然后，通过局部矢量模式（LVP）的直方图解释每个区域。在深度和RGB模式中，像素的深度值和像素的灰度级分别用于提取LVP。通过计算骨骼图像各关节点的各种欧几里得距离来提取骨骼特征。然后，使用得分级别融合将通过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，结果说明了该方法的可接受性能。

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