| 宋晓勇,孙学宏,刘丽萍,覃国车,余彤,李享国.基于多细粒度双流网络的行人重识别[J].电子测量与仪器学报,2025,39(8):250-257 |
| 基于多细粒度双流网络的行人重识别 |
| Pedestrian re-identification based on a multi-granularity dual-stream network |
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| DOI： |
| 中文关键词: 遮挡行人重识别 多细粒度特征提取 双流网络 特征融合 |
| 英文关键词:occluded person re-identification multi-granularity feature extraction dual-stream network feature fusion |
| 基金项目:宁夏回族自治区重点研发计划项目（2022BEG02052）资助 |
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| Author | Institution |
| Song Xiaoyong | School of Electronic and Electrical Engineering, Ningxia University, Yinchuan 750021, China |
| Sun Xuehong | 1.School of Electronic and Electrical Engineering, Ningxia University, Yinchuan 750021, China;
2.Key Laboratory of Intelligent Perception for Desert Information, Yinchuan 750021, China |
| Liu Liping | 1.School of Electronic and Electrical Engineering, Ningxia University, Yinchuan 750021, China;
2.Key Laboratory of Intelligent Perception for Desert Information, Yinchuan 750021, China |
| Qin Guoche | School of Electronic and Electrical Engineering, Ningxia University, Yinchuan 750021, China |
| Yu Tong | School of Electronic and Electrical Engineering, Ningxia University, Yinchuan 750021, China |
| Li Xiangguo | School of Electronic and Electrical Engineering, Ningxia University, Yinchuan 750021, China |
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| 中文摘要: |
| 在实际监控场景中行人重识别任务面临诸多挑战，如部分图像遮挡（树木、人、汽车、小物体等）导致识别过程中关键信息丢失和识别精度下降。在遮挡行人重识别任务中，通常采用局部联合全局特征或姿态估计器的方法来解决识别精度低等问题，虽然在部分遮挡情况下利用单流网络有较好的识别性能，但在处理过程中未能充分挖掘剩余关键特征信息。为此，提出了一种基于多细粒度双流网络的遮挡行人重识别方法，通过设计多细粒度局部特征提取策略、双流特征处理网络和特征权重融合模块来增强关键特征信息提取能力。该方法采用视觉Transformer（ViT）提取全局特征，并将其划分为多组局部特征。随后，各组局部特征分别经过双流特征处理网络，将通过双流网络的特征进行特征权重融合，从而更有效地挖掘关键特征信息。在Occluded-Duke、Market-1501、DukeMTMC-reID和MSMT17数据集上实验结果证明所提方法的有效性与合理性，平均精度均值（mAP）/Rank-1指标分别达到了61.3%/68.3、89.0%/95.2%、82.5%/91.1%和66.8%/84.5%。 |
| 英文摘要: |
| In real surveillance scenarios, pedestrian re-identification tasks face numerous challenges, such as partial image occlusions (trees, people, cars, small objects, etc.) that lead to the loss of key information and a decline in recognition accuracy. To address issues like low recognition accuracy in occluded pedestrian re-identification tasks, methods that combine local and global features or use pose estimators are commonly employed. Although single-stream networks can achieve good recognition performance under partial occlusions, they fail to fully exploit the remaining critical feature information during processing. Therefore, we propose an occluded pedestrian re-identification method based on a multi-granularity dual-stream network. By designing a multi-granularity local feature extraction strategy, a dual-stream feature processing network, and a feature weight fusion module, the ability to extract key feature information is enhanced. This method employs a vision Transformer (ViT) to extract global features and divides them into multiple groups of local features. Subsequently, each group of local features is processed through a dual-stream feature processing network. The features obtained from the dual-stream network are then fused using a feature weight fusion mechanism, thereby more effectively mining key feature information. Experimental results on the Occluded-Duke, Market-1501, DukeMTMC-reID, and MSMT17 datasets demonstrate the effectiveness and validity of the proposed method, achieving mAP/Rank-1 indicators of 61.3%/68.3%, 89.0%/95.2%, 82.5%/91.1%, and 66.8%/84.5%, respectively. |
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