摘要
为了让网络捕捉到更有效的内容来进行行人的判别,该文提出一种基于阶梯型特征空间分割与局部分支注意力网络(SLANet)机制的多分支网络来关注局部图像的显著信息。首先,在网络中引入阶梯型分支注意力模块,该模块以阶梯型对特征图进行水平分块,并且使用了分支注意力给每个分支分配不同的权重。其次,在网络中引入多尺度自适应注意力模块,该模块对局部特征进行处理,自适应调整感受野尺寸来适应不同尺度图像,同时融合了通道注意力和空间注意力筛选出图像重要特征。在网络的设计上,使用多粒度网络将全局特征和局部特征进行结合。最后,该方法在3个被广泛使用的行人重识别数据集Market-1501,DukeMTMC-reID和CUHK03上进行验证。其中在Market-1501数据集上的mAP和Rank-1分别达到了88.1%和95.6%。实验结果表明,该文所提出的网络模型能够提高行人重识别准确率。
In order to make the network capture more effective content distinguish pedestrians,this paper proposes a multi-branch network based on Stepped feature space segmentation and Local Branch Attention Network(SLANet)mechanism to pay attention to the salient information of local images.First of all,a stepped branch attention module is introduced into the network.This module blocks the feature map horizontally in a stepped manner,and branch attention is used to assign different weights to each branch.Secondly,a multiscale adaptive attention module is introduced into the network,which processes local features and adapts the size of the receptive field to adapt to images of different scales.Meanwhile,channel attention and spatial attention are combined to screen out the important features of the image.In the design of network,the multigranularity network is used to combine the global feature with the local feature.Finally,the method is validated on three widely used person re-identification data sets Market-1501,DukeMTMC-reID and CUHK03.Among them,mAP and Rank-1 on market-1501 data set reach 88.1%and 95.6%respectively.The experimental results show that the proposed network model can improve the accuracy of person re-identification.
作者
石跃祥
周玥
SHI Yuexiang;ZHOU Yue(School of Computer Science and School of Cyberspace Security,Xiangtan University,Xiangtan 411105,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2022年第1期195-202,共8页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61602397,61502407)。
关键词
行人重识别
特征空间分割
注意力机制
局部特征
Person re-identification
Feature space segmentation
Attention mechanism
Local features
