【摘要】 针对真实环境中由于复杂背景和物体遮挡、角度变换、行人姿态变化带来的行人重识别(person re-identification, person re-ID)问题，设计了基于通道注意力(efficient channel attention, ECA)机制和多尺度卷积(poly-scale convolution, PSConv)的行人重识别模型。首先利用残差网络提取全局特征，在网络末端加入基于ECA机制及PSConv的特征融合模块，将全局特征和该模块提取的全局特征进行融合，之后将新的全局特征进行分割得到局部特征，最后将新的全局特征和分割得到的局部特征融合得到最终特征，并计算损失函数。模型在Market1501和DukeMTMC-reID数据集上进行实验验证。在Market1501数据集中，Rank-1和平均精度均值分别达到94.3%和85.2%,在DukeMTMC-reID数据集中，上述两参数分别达到86.3%和75.4%。实验结果可知，该模型可应对实际环境中的复杂情况，增强行人特征的辨别力，有效提高行人重识别的准确率和精度。更多还原

【Abstract】 Aiming at the problem of person re-identification(person re-ID) caused by complex background and object occlusion, angle transformation and pedestrian posture change in real environment, a person re-identification model based on efficient channel attention(ECA) and poly-scale convolution(PSConv) is designed.Firstly, the residual network is used to extract the global features, and a feature fusion module based on PSConv and ECA is added at the end of the network.The global features are fuzed with the global features extracted from the module to get a new global feature, and then the new global feature is segmented to obtain local features.Finally, the new global feature and the local feature are fused to get the final feature, and the loss function is calculated.The experiment is verified on Market1501 and DukeMTMC-reID data set.Rank-1 and mean average precision reach 94.3 % and 85.2 % respectively on Market1501 data set, and 86.3 % and 75.4 % respectively on DukeMTMC-reID data set.The results show that the model can deal with the complex situation in the actual environment, enhance the discrimination of pedestrian features, and effectively improve the accuracy and precision of pedestrian recognition.更多还原