基于全局特征改进的行人重识别
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Improved Person Re-identification Based on Global Feature
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    摘要:

    由于行人重识别面临姿态变化、遮挡干扰、光照差异等挑战, 因此提取判别力强的行人特征至关重要. 本文提出一种在全局特征基础上进行改进的行人重识别方法, 首先, 设计多重感受野融合模块充分获取行人上下文信息, 提升全局特征辨别力; 其次, 采用GeM池化获取细粒度特征; 最后, 构建多分支网络, 融合网络不同深度的特征预测行人身份. 本文方法在Market1501和DukeMTMC-ReID两大数据集上的mAP指标分别达到83.8%和74.9%. 实验结果表明, 本文方法有效改进了基于全局特征的模型, 提升了行人重识别的识别准确率.

    Abstract:

    Person re-identification faces challenges such as posture change, occlusion interference, and illumination difference, and thus it is very important to extract pedestrian features with strong discriminability. In this paper, an improved person re-identification method based on global features is proposed. Firstly, a multi-receptive field fusion module is designed to fully obtain pedestrian context information and improve the global feature discriminability. Secondly, generalized mean (GeM) pooling is used to obtain fine-grained features. Finally, a multi-branch network is constructed, and the features of different depths of the network are fused to predict the identity of pedestrians. The mAP indexes of this method on Market1501 and DukeMTMC-ReID are 83.8% and 74.9%, respectively. The experimental results show that the proposed method can effectively improve the model based on global features and raise the recognition accuracy of person re-identification.

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张晓涵.基于全局特征改进的行人重识别.计算机系统应用,2022,31(5):298-303

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  • 收稿日期:2021-08-04
  • 最后修改日期:2021-08-31
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  • 在线发布日期: 2022-04-11
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