一种基于ViT技术的被遮挡行人目标重识别方法

ViT-based Method for Occluded Pedestrian Re-identification

摘要

行人目标重识别(ReID)是指在不同场景中匹配同一行人目标的技术.针对在有遮挡物的情况下依赖全局信息方式处理行人目标细节特征时,出现的局部信息表达能力受限问题,提出了一个基于ViT特征增强的ReID方法,主要包括:1)设计一个新型的跨尺度空洞融合模块(Dimensional Feature Reinforcement Module,CDFM),通过多维度重加权对输入特征进行优化,提升特征表达能力;2)提出一个全局与局部特征协同算法,用以提升模型的性能和鲁棒性;.该方法结合了Transformer模块对全局依赖的建模能力和CNN在捕获局部细节特征上的优势,从而增强了特征信息的流动性和表达能力;3)提出一个动态加权损失函数,通过可见区域感知对比机制明确增强可见区域特征一致性,引入动态难例采样策略缓解遮挡噪声干扰,并融合通道注意力权重优化特征对齐,进一步提升模型在遮挡场景下的判别力.实验结果表明,所提出的方法在多个主流有遮挡的ReID数据集上表现出更强的性能优势.

Abstract

Person Re-Identification (ReID) refers to the technology of matching the same pedestrian across different scenarios.To address the limitations of local feature representation caused by relying solely on global information in handling pedestrian details under occlusion scenarios,this paper proposes a ViT-enhanced ReID method.The key contributions include:1) A novel Cross-scale Dilated Fusion Module (CDFM) that optimizes input features through multi-dimensional re-weighting and integrates multi-scale dilated convolutional branches to enhance feature discriminability;2) A Global-Local Feature Collaboration Module combining Transformer blocks and lightweight CNN layers to leverage the complementary strengths of global dependency modeling by Transformers and local detail feature extraction by CNNs,thereby improving feature fusion and robustness;3) A Dynamic Weighted Loss Function that introduces a visibility-aware contrastive learning mechanism to enforce consistency in visible regions,adopts a dynamic hard example mining strategy to mitigate occlusion-induced noise interference,and incorporates channel attention weights for refined feature alignment,significantly enhancing discriminative power in occlusion scenarios.Experimental results demonstrate that the proposed method achieves superior performance on multiple mainstream occluded ReID benchmarks,including Occluded-Duke and Occluded-REID,outperforming existing state-of-the-art methods in both Rank-1 accuracy and mAP metrics.

关键词

行人重识别 / ViT / 跨尺度空洞融合 / 全局与局部特征协同 / 动态加权损失

Key words

Person Re-Identification(ReID) / ViT / cross-scale dilated fusion / lightweight convolution / contrastive weighting loss

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基金资助

湖南省自然科学基金项目(2024JJ7154)资助;湖南衣朵云商品智能化项目(h2024-003)资助.