Abstract
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.
Abstract (translated)
本文研究了一种结合人体解析、特征提取和相似度估计方案的行人重识别（re-id）方法。该方法的一个显著特点是其计算需求低，因此可以在边缘设备上实现。该方法允许使用可解释特征直接比较特定图像区域，这些特征包括颜色和纹理通道。建议在CIE-Lab色彩空间中分析和比较颜色，并通过直方图平滑来减少噪声。为了进行纹理分析，提出了一种预配置的潜在空间（LS）监督自编码器（SAE），它将输入纹理编码为LS点。这相比于简单的标签对比，可以获得更准确的相似度测量结果。所提方法不需要依赖照片或其他重识别数据来进行训练，使其完全不依赖于特定的重识别数据集。通过在Market1501数据集上计算rank-1、rank-10和mAP重识别指标来验证了该方法的有效性。实验结果显示，与传统深度学习方法相比，其性能可比，并讨论了进一步改进该方法的可能性。
URL
https://arxiv.org/abs/2412.05076
