无需训练数据，用颜色纹理分析实现边缘设备部署的行人重识别
Improving analytical color and texture similarity estimation methods for dataset-agnostic person reidentification
- 基于颜色纹理的可解释特征，直接比对图像局部区域
- 在Market1501上达到与深度学习相当的rank-1和mAP性能
- 无需任何重识别数据训练，适合资源受限场景
本文提出一种结合人体解析、解析特征提取与相似性估计的行人重识别方法。该方法计算量低，适用于边缘设备部署。通过在CIE-Lab色彩空间中使用直方图平滑处理颜色特征以降噪，并设计一种预配置潜在空间（LS）的有监督自编码器（SAE）进行纹理分析，将输入纹理编码为潜在空间中的点，从而获得比简单标签比较更精确的相似性度量。该方法不依赖任何重识别数据进行训练，具备完全的数据集无关性。在Market1501数据集上验证了其有效性，计算了rank-1、rank-10和mAP指标，结果与传统深度学习方法相当，同时讨论了进一步优化的可能性。
原文摘要 · Abstract (English)
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.
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