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Using latent features for short-term person re-identification with RGB-D cameras

机译：使用潜在功能通过RGB-D摄像机进行短期人员重新识别

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This paper presents a system for people re-identification in uncontrolled scenarios using RGB-depth cameras. Compared to conventional RGB cameras, the use of depth information greatly simplifies the tasks of segmentation and tracking. In a previous work, we proposed a similar architecture where people were characterized using color-based descriptors that we named bodyprints. In this work, we propose the use of latent feature models to extract more relevant information from the bodyprint descriptors by reducing their dimensionality. Latent features can also cope with missing data in case of occlusions. Different probabilistic latent feature models, such as probabilistic principal component analysis and factor analysis, are compared in the paper. The main difference between the models is how the observation noise is handled in each case. Re-identification experiments have been conducted in a real store where people behaved naturally. The results show that the use of the latent features significantly improves the re-identification rates compared to state-of-the-art works.
机译：本文提出了一种使用RGB深度相机在不受控制的场景中对人员进行重新识别的系统。与传统的RGB相机相比，深度信息的使用大大简化了分割和跟踪的任务。在之前的工作中，我们提出了一种类似的体系结构，其中使用基于人体彩绘的基于颜色的描述符对人进行表征。在这项工作中，我们建议使用潜在特征模型以通过减小车身特征码的维数来从车身特征码描述符中提取更多相关信息。在遮挡的情况下，潜在特征也可以应对丢失的数据。本文比较了不同的概率潜在特征模型，例如概率主成分分析和因子分析。模型之间的主要区别在于每种情况下如何处理观察噪声。重新识别实验已经在人们自然行为的真实商店中进行。结果表明，与最新技术作品相比，潜在特征的使用显着提高了重新识别率。

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