融合卷积注意力和Transformer架构的行人重识别方法

A person re-identification method for fusing convolutional attention and Transformer architecture

行人重识别技术是智能安防系统中的重要方法之一,为构建一个适用各种复杂场景的行人重识别模型,基于现有的卷积神经网络和Transformer模型,提出一种融合卷积注意力和Transformer(FCAT)架构的方法,以增强Transformer对局部细节信息的关注。所提方法主要将卷积空间注意力和通道注意力嵌入Transformer架构中,分别加强对图像中重要区域的关注和对重要通道特征的关注,以进一步提高Transformer架构对局部细节特征的提取能力。在3个公开行人重识别数据集上的对比消融实验证明,所提方法在非遮挡数据集上取得了与现有方法相当的结果,在遮挡数据集上的性能得到显著提升。所提方法更加轻量化,在不增加额外计算量和模型参数的情况下,推理速度得到了提升。

Person Re-identification technology is one of the important methods in intelligent security systems.In order to build a person re-identification model suitable for various complex scenarios,this article proposed a method of Fusing Convolutional Attention and Transformer architecture(FCAT)based on existing convolutional neural networks and Transformer models to enhance the Transformer’s attention to local detail information.This method mainly improves the transformer's ability to extract local detail features indirectly by embedding convolutional space attention and channel attention respectively to enhance the attention to important regions and important channel features in the image.Comparative ablation experiments on three publicly available pedestrian re-identification datasets demonstrate that the proposed method achieves comparable results on non-occluded datasets and significantly improves performance on occluded datasets.Additionally,the proposed model is more lightweight,leading to improved inference speed without increasing additional computational load or model parameters.