多层级重叠条纹特征融合的行人重识别

doi:10.3778/j.issn.1673-9418.2005053

摘要/Abstract

摘要：

针对行人重识别（Person-ReID）过程中，基于局部特征的方法在提取行人特征时因信息缺失导致鲁棒性和判别力不足的问题，提出一种多层级重叠条纹特征融合的行人重识别算法。训练阶段，对骨架网络不同阶段的输出特征图进行水平均等分割，再提取重叠条纹特征以弥补丢失的信息；使用三种损失函数对不同的特征向量进行监督训练，以约束类内距离。此外，设计组归一化模块来消除不同损失函数在优化方向上存在的差异，从而提取到更恰当的共享特征。推理阶段，将多个特征向量融合成一个新的特征向量，再进行相似性判断。将该方法在Market-1501、DukeMTMC-reID数据集上进行有效性实验验证并对结果进行分析。所提算法能够提高行人重识别的准确率，模型所提取的特征具有较强的鲁棒性和判别力。

关键词: 重叠条纹特征, 特征融合, 联合学习, 行人重识别（Person-ReID）, 深度学习

Abstract:

Most of the local features based person re-identification (Person-ReID) methods have the problem of lack of robustness and discriminability due to the distortion of information in the procedure of extraction of pedestrian features. In this paper, a novel Person-ReID algorithm based on multi-level feature fusion with overlapping stripes is proposed. In the training process, the output feature maps from different layers of the backbone network are segmented equally in the vertical axis. The features with overlapping stripes are extracted to compensate the loss of information. Three different loss functions are used for different feature vectors in the procedure of the training to minimize the intra-calss distance. Group normalization modules are applied to reducing the optimization differences within various loss functions for obtaining appropriate shared features. In the inference stage, multiple feature vectors are fused into a new feature vector, and the similarity is calculated. This algorithm is performed on Market-1501 and DukeMTMC-reID datasets with the analysis of experimental results. The proposed algorithm can improve the accuracy of Person-ReID, and the features extracted by the model have strong robustness and discriminability.

Key words: feature with overlapping stripes, feature fusion, joint learning, person re-identification (Person-ReID), deep learning

陈璠, 彭力. 多层级重叠条纹特征融合的行人重识别[J]. 计算机科学与探索, 2021, 15(9): 1753-1761.

CHEN Fan, PENG Li. Person Re-identification Based on Multi-level Feature Fusion with Overlapping Stripes[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(9): 1753-1761.

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