摘要
针对行人重识别中存在遮挡及行人判别特征层次单调的问题,在IBN-Net50-a网络的基础上,提出了一种结合随机遮挡和多粒度特征融合的网络模型。通过对输入图像进行随机遮挡处理,模拟行人被遮挡的真实情景,以增强应对遮挡的鲁棒性;将网络分为全局分支、局部粗粒度互融分支和局部细粒度互融分支,提取全局显著性特征,同时补充局部多粒度深层特征,丰富行人判别特征的层次性;进一步挖掘局部多粒度特征间的相关性进行深度融合;联合标签平滑交叉熵损失和三元组损失训练网络。在3个标准公共数据集和1个遮挡数据集上,将所提方法与先进的行人重识别方法进行比较,实验结果表明:在Market1501、DukeMTMC-reID、CUHK03标准公共数据集上,所提方法的Rank-1分别达到了95.2%、89.2%、80.1%,在遮挡数据集Occluded-Duke上,所提方法的Rank-1和mAP分别达到了60.6%和51.6%,均优于对比方法,证实了方法的有效性。
Aiming at the problems of occlusion and monotony of pedestrian discriminative feature hierarchy in person re-identification,this paper proposes a method combining random occlusion and multi-granularity feature fusion based on the IBN-Net50-a network.First,in order to enhance the robustness against occlusion,random occlusion processing is performed on the input images to simulate the real scene of pedestrians being occluded.Secondly,the network includes a global branch,a local coarse-grained fusion branch and a local fine-grained fusion branch,which can extract global salient features while supplementing local multi-grained deep features,enriching the hierarchy of pedestrian discrimination features.Furthermore,further mining the correlation between local multi-granularity features for deeper fusion.Finally,the label smoothing loss and triplet loss jointly train the network.Comparing the proposed method with current state-of-the-art person re-identification algorithms on three standard public datasets and one occlusion dataset.The experimental results show that the Rank-1 of the proposed algorithm on Market1501,DukeMTMC-reID and CUHK03 is 95.2%,89.2%and 80.1%,respectively.In Occluded-Duke dataset,Rank-1 and mAP achieved 60.6%and 51.6%.The experimental results are better than those of the compared methods,which fully confirm the effectiveness of the proposed method.
作者
张楠
程德强
寇旗旗
马浩辉
钱建生
ZHANG Nan;CHENG Deqiang;KOU Qiqi;MA Haohui;QIAN Jiansheng(School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China;School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221116,China)
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2023年第12期3511-3519,共9页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家自然科学基金(51774281)。
关键词
行人重识别
全局特征
随机遮挡
局部特征融合
联合损失
person re-identification
global features
random occlusion
local feature fusion
joint loss
