This paper studies a combined person reidentification (re-id) method that uses human parsing, analytical feature extraction and similarity estimation schemes. One of its prominent features is its low computational requirements so it can be implemented on edge devices. The method allows direct comparison of specific image regions using interpretable features which consist of color and texture channels. It is proposed to analyze and compare colors in CIE-Lab color space using histogram smoothing for noise reduction. A novel pre-configured latent space (LS) supervised autoencoder (SAE) is proposed for texture analysis which encodes input textures as LS points. This allows to obtain more accurate similarity measures compared to simplistic label comparison. The proposed method also does not rely upon photos or other re-id data for training, which makes it completely re-id dataset-agnostic. The viability of the proposed method is verified by computing rank-1, rank-10, and mAP re-id metrics on Market1501 dataset. The results are comparable to those of conventional deep learning methods and the potential ways to further improve the method are discussed.

翻译：本文研究了一种结合人体解析、解析特征提取与相似度估计方案的行人重识别方法。其显著特征之一是计算需求低，可在边缘设备上部署。该方法允许使用由颜色和纹理通道构成的可解释特征直接比较特定图像区域。文中提出在CIE-Lab色彩空间中使用直方图平滑进行降噪的颜色分析与比较方案。针对纹理分析，提出了一种新颖的预配置潜在空间监督自编码器，可将输入纹理编码为潜在空间点。与简单的标签比较相比，该方法能获得更精确的相似度度量。所提方法在训练过程中不依赖照片或其他重识别数据，实现了完全的数据集无关性。通过在Market1501数据集上计算rank-1、rank-10和mAP重识别指标，验证了该方法的可行性。实验结果与传统深度学习方法相当，文中还讨论了进一步改进该方法的潜在途径。