行人重识别技术是在跨摄像头的不同场景下对特定行人的识别和检索，被广泛应用在公共安全、智能安防和人机交互等领域。由于在实际场景中存在姿态、光照、遮挡、分辨率和视角变化等情况，会造成行人的外观特征差异较大，从而导致行人重识别的识别性能下降。本文基于深度学习的方法，从网络结构的设计和损失函数两个方面提出以下两种改进方法：

（1）针对提取的行人图像特征较为单一的问题，通过结合全局特征和多粒度局部特征，提出了一种基于改进多分支网络结构的行人重识别方法。该方法以ResNet50-IBN-a作为骨干网络，多分支网络结构共分为Top DropBlock分支、全局特征分支和两个局部特征分支，有效提取不同粒度的局部细节特征和全局特征，从而获得更全面的特征表示。同时，采用softmax损失和三元组损失函数对模型进行训练。

（2）针对行人重识别中的行人遮挡或者姿态变化等问题，通过分析行人图像切块的局部特征相关性，提出了一种基于局部特征上下文相关性的行人重识别方法。设计局部特征上下文相关性策略，将切分后的相邻水平条带进行组合，获得相邻局部特征之间的关联性，从而得到更丰富的特征表示。同时，采用softmax损失、中心损失和三元组损失函数对模型进行联合训练，进一步提升模型的分类效果和泛化能力。

将所提算法在Market1501、DukeMTMC-reID和CHUK03数据集上进行验证，并与多个主流算法对比。实验结果表明，改进的多分支网络结构能够有效地提取行人图像的细节特征，局部特征上下文相关性策略可以降低遮挡或姿态变化等对行人重识别的影响，有效提升行人重识别的算法性能。

Person re-identification technology is the identification and retrieval of specific pedestrians in different scenarios across cameras, and is widely used in public safety, intelligent security, and human-computer interaction. Due to the presence of posture, illumination, occlusion, resolution, and viewing angle changes in the actual scene, the appearance characteristics of pedestrians will be quite different, and the performance of person re-identification will decrease. Based on the method of deep learning, this paper proposes the following two improvement methods from the two aspects of network structure design and loss function:

(1) Aiming at the problem that the extracted pedestrian image features are relatively single, by combining global features and multi-granularity local features, a pedestrian re-recognition method based on an improved multi-branch network structure is proposed.This method uses ResNet50-IBN-a as the backbone network. The multi-branch network structure is divided into Top DropBlock branch, global feature branch and two local feature branches. It can effectively extract local detailed features and global features of different granularities to obtain a more comprehensive feature representation. At the same time, the softmax loss and triplet loss function are used to train the model.

(2) Aiming at the problems of pedestrian occlusion or posture change in pedestrian re-recognition, a method of pedestrian re-recognition based on the context relevance of local features is proposed by analyzing the correlation of the local features of the pedestrian image segmentation.Design the local feature context correlation strategy, combine the adjacent horizontal strips after segmentation to obtain the correlation between adjacent local features, thereby obtaining a richer feature representation. At the same time, the softmax loss, center loss and triple loss function are used to jointly train the model to further improve the classification effect and generalization ability of the model.

Comparing with several state-of-the-art person re-identification methods on Market1501, DukeMTMC-reID and CHUK03 datasets, the experimental results show that our improved algorithm can effectively increase the performance of person re-identification. The improved multi-branch network structure can effectively extract the detailed features of pedestrian images and the local feature context correlation strategy can reduce the impact of occlusion or posture change.

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