基于注意力机制与多尺度特征融合的行人重识别方法

针对行人重识别中因遮挡、姿态变化使模型特征无法充分表达行人信息的问题,提出了基于注意力机制与多尺度特征融合的行人重识别方法.首先使用改进的骨干网络R-ResNet50提取图像特征;其次,抽取网络不同尺度的特征层嵌入注意力机制DANet,使模型更关注于重点信息;最后,对提取出的关键特征进行多尺度特征融合,实现特征间的优势...

Person re-identification method based on attention mechanism and multi-scale feature fusion

For the problem that model features cannot fully express the person information due to occlusion and posture change in person re-identification, the person re-identification method based on the attention mechanism and multi-scale features fusion is proposed. In this method, firstly the improved backbone network (R-ResNet50) was used to extract image features; secondly, the feature layers of the network at different scales was extracted to embed in the attention mechanism (DANet), so that the model paid more attention to the key information; finally, the extracted key features were fused with multi-scale features to achieve complementary advantages among features, and the multi-loss function strategy of cross entropy loss, difficult sample triplet loss and center loss was used to train the network model. The experimental results show that the Rank-1 and mAP of this method on the Market1501 and DukeMTMC-ReID dataset are 92.7%, 80.4% and 86.4%, 71.0% respectively, so the features extracted from the model are more discriminant and the recognition rate is higher.