基于特征定位与融合的行人重识别算法

doi:10.11896/jsjkx.210100132

摘要/Abstract

摘要： 行人外观属性是区分行人差异的重要语义信息。行人属性识别在智能视频监控中有着至关重要的作用,可以帮助我们对目标行人进行快速的筛选和检索。在行人重识别任务中,可以利用属性信息得到精细的特征表达,从而提升行人重识别的效果。文中尝试将行人属性识别与行人重识别相结合,寻找一种提高行人重识别性能的方法,进而提出了一种基于特征定位与融合的行人重识别框架。首先,利用多任务学习的方法将行人重识别与属性识别结合,通过修改卷积步长和使用双池化来提升网络模型的性能。其次,为了提高属性特征的表达能力,设计了基于注意力机制的平行空间通道注意力模块,它不仅可以在特征图上定位属性的空间位置,而且还可以有效地挖掘与属性关联度较高的通道特征,同时采用多组平行分支结构减小误差,进一步提高网络模型的性能。最后,利用卷积神经网络设计特征融合模块,将属性特征与行人身份特征进行有效融合,以获得更具鲁棒性和表达力的行人特征。实验在两个常用的行人重识别数据集DukeMTMC-reID和Market-1501上进行,结果表明,所提方法在现有的行人重识别方法中处于领先水平。

关键词: 多任务学习, 特征定位, 特征融合, 行人重识别, 行人属性

Abstract: Pedestrian appearance attributes are important semantic information distinguishing pedestrian differences.Pedestrian attribute recognition plays a vital role in intelligent video surveillance,which can help us quickly screen and retrieve target pedestrians.In the task of person re-identification,we can use attribute information to obtain fine feature expressions,thereby improving the effect of pedestrian re-identification.This paper attempts to combine pedestrian attribute recognition with person re-identification,looking for a way to improve the performance of person re-identification,and proposes a person re-identification framework based on feature positioning and fusion.Firstly,we use the method of multi-task learning to combine person re-identification with attribute recognition,and improve the performance of the network model by modifying the convolution step size and using double pooling.Secondly,to improve the expression ability of attribute features,a parallel spatial channel attention module based on the attention mechanism is designed.It can not only locate the spatial position of the attribute on the feature map,but also can effectively mine the channel with higher correlation with the attribute features,and uses multiple groups of parallel branch structure to reduce errors and further improve the performance of the network model.Finally,we use the convolutional neural network to design the feature fusion module to effectively integrate the attribute features and pedestrian identity features to obtain more robust and expressive pedestrian features.The experiment is conducted on two commonly used person re-identification datasets DukeMTMC-reID and Market-1501.The results show that this method is at the leading level among the existing person re-identification methods.

Key words: Feature fusion, Feature location, Multi-task learning, Pedestrian attribute, Person re-identification

中图分类号:

TP399

杨晓宇, 殷康宁, 候少麒, 杜文仪, 殷光强. 基于特征定位与融合的行人重识别算法[J]. 计算机科学, 2022, 49(3): 170-178. https://doi.org/10.11896/jsjkx.210100132

YANG Xiao-yu, YIN Kang-ning, HOU Shao-qi, DU Wen-yi, YIN Guang-qiang. Person Re-identification Based on Feature Location and Fusion[J]. Computer Science, 2022, 49(3): 170-178. https://doi.org/10.11896/jsjkx.210100132

参考文献

[1]LI W,ZHAO R,XIAO T,et al.Deepreid:Deep filter pairingneural network for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Columbus,OH:IEEE,2014:152-159.
[2]CHENG D,GONG Y,ZHOU S,et al.Person Reidentification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas,NV:IEEE,2016:1335-1344.
[3]KARAMAN S,LISANTI G,BAGDANOV A.Leveraging local neighborhood topology for large scale person re-identification[C]//Pattern Recognition.Columbus,OH:IEEE,2014:3767-3778.
[4]LIN J,REN L,LU J,et al.Consistent-Aware Deep Learning for Person Re-identification in a Camera Network[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Honolulu,HI:IEEE,2017:3396-3405.
[5]OU X,MA Q,WANG Y.Improving person re-identification by multi-task learning[J].Multimedia Tools & Applications,2019,347:109-118.
[6]WOO S,PARK J,LEE J Y,et al.Cbam:Convolutional blockattention module[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:3-19.
[7]KIPF T,WELLING M.Semi-Supervised Classification withGraph Convolutional Networks[C]//ICLR.2017.
[8]CARUANA R.Multitask learning[J].Machine Learning,1997,28(1):41-75.
[9]MISRA I,SHRIVASTAVA A,GUPTA A,et al.Cross-stitchnetworks for multi-task learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:3994-4003.
[10]LIU S,JOHNS E,DAVISON A J.End-to-end multi-task lear-ning with attention[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:1871-1880.
[11]GAO Y,MA J,ZHAO M,et al.Nddr-cnn:Layerwise feature fusing in multi-task cnns by neural discriminative dimensionality reduction[C]//Proceedings of the IEEE Conference on Compu-ter Vision and Pattern Recognition.2019:3205-3214.
[12]KOKKINOS I.Ubernet:Training a universal convolutional neural network for low-,mid-,and high-level vision using diverse datasets and limited memory[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:6129-6138.
[13]LONG M,CAO Z,WANG J,et al.Learning multiple tasks with multilinear relationship networks[C]//Advances in Neural Information Processing Systems.2017:1594-1603.
[14]BRAGMAN F J S,TANNO R,OURSELIN S,et al.Stochastic filter groups for multi-task cnns:Learning specialist and gene-ralist convolution kernels[C]//Proceedings of the IEEE International Conference on Computer Vision.2019:1385-1394.
[15]LU Y,KUMAR A,ZHAI S,et al.Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:5334-5343.
[16]ZAMIR A R,SAX A,SHEN W,et al.Taskonomy:Disentangling task transfer learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:3712-3722.
[17]HOSPEDALES T,LAYNE R,GONG S.Re-id:Hunting attri-butes in the wild[C]//British Machine Vision Conference.2014.
[18]LAYNE R,HOSPEDALES T M,GONG S,et al.Person re-identification by attributes[C]//Bmvc.2012:8.
[19]SU C,YANG F,ZHANG S,et al.Multi-task learning with low rank attribute embedding for person re-identification[C]//Proceedings of the IEEE Intenational Conference on Computer Vision.2015:3739-3747.
[20]LI H,TANG M,LIN J W,et al.Cross-modality Person Re-identification Framework Based on Im-proved Hard Triplet Loss[J].Computer Science,2020,47(10):180-186.
[21]KHAMIS S,KUO C H,SINGH V K,et al.Joint learning for attribute-consistent person re-identification[C]//European Conference on Computer Vision.Springer,Cham,2014:134-146.
[22]FRANCO A,OLIVEIRA L.Convolutional covariance features:Conception,integration and performance in person reidentification[J].Pattern Recognition,2017,61:593-609.
[23]SCHUMANN A,STIEFELHAGEN R.Person re-identification by deep learning attribute-complementary information[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).Honolulu,HI:IEEE,2017:1435-1443.
[24]WANG J,ZHU X,GONG S,et al.Transferable joint attribute-identity deep learning for unsupervised person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City,UT:IEEE,2018:2275-2284.
[25]HE K,ZHANG X,REN S,et al.Deep Residual Learning forImage Recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR).2016:770-778.
[26]WANG D,ZHOU D K,HUANG Y D,et al.Multi-scale Multi-granularity Feature for Pedestrian Re-identification[J].Compu-ter Science,2021,48(7):238-244.
[27]LIN M,CHEN Q,YAN S.Network In Network[J].Computer Science,2013.
[28]IOFFE S,SZEGEDY C.Batch normalization:Accelerating deep network training by reducing internal covariate shift[J].arXiv:1502.03167,2015.
[29]GLOROT X,BORDES A,BENGIO Y.Deep sparse rectifierneural networks[C]//Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics.2011:315-323.
[30]LI Q,HAN Z,WU X M.Deeper insights into graph convolutional networks for semi-supervised learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2018.
[31]ZHENG L,SHEN L,TIAN L,et al.Scalable person re-identification:A benchmark[C]//Proceedings of the IEEE Internatio-nal Conference on Computer Vision.2015:1116-1124.
[32]RISTANI E,SOLERA F,ZOU R,et al.Performance measures and a data set for multi-target,multi-camera tracking[C]//European Conference on Computer Vision.Springer,Cham,2016:17-35.
[33]ZHENG Z,ZHENG L,YANG Y.Unlabeled samples generatedby gan improve the person re-identification baseline in vitro[C]//Proceedings of the IEEE International Conference on Computer Vision (ICCV).Venice:IEEE,2017:3754-3762.
[34]SUN Y,ZHENG L,DENG W,et al.Svdnet for pedestrian retrieval[C]//Proceedings of the IEEE International Conference on Computer Vision (ICCV).Venice:IEEE,2017:3800-3808.
[35]ZHENG Z,ZHENG L,YANG Y.Pedestrian alignment network for large-scale person re-identification[J].IEEE Transactions on Circuits and Systems for Video Technology,2018,29(10):3037-3045.
[36]SI J,ZHANG H,LI C G,et al.Dual attention matching network for context-aware feature sequence based person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:5363-5372.
[37]SUN Y,ZHENG L,YANG Y,et al.Beyond partmodels:Person retrieval with refined part pooling (and a strong convolutional baseline)[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:480-496.
[38]SUN Y,XU Q,LI Y,et al.Perceive where to focus:Learningvisibility-aware part-level features for partial person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:393-402.
[39]KALAYEH M M,BASARAN E,GÖKMEN M,et al.Humansemantic parsing for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:1062-1071.
[40]HOU R,MA B,CHANG H,et al.Interaction-and-aggregation network for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:9317-9326.
[41]TAY C P,ROY S,YAP K H.Aanet:Attribute attention network for person re-identifications[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:7134-7143.
[42]DAI Z,CHEN M,GU X,et al.Batch DropBlock network forperson re-identification and beyond[C]//Proceedings of the IEEE International Conference on Computer Vision(ICCV).2019:3691-3701.

相关文章 15

[1]	张颖涛, 张杰, 张睿, 张文强. 全局信息引导的真实图像风格迁移 Photorealistic Style Transfer Guided by Global Information 计算机科学, 2022, 49(7): 100-105. https://doi.org/10.11896/jsjkx.210600036
[2]	程成, 降爱莲. 基于多路径特征提取的实时语义分割方法 Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction 计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157
[3]	孟月波, 穆思蓉, 刘光辉, 徐胜军, 韩九强. 基于向量注意力机制GoogLeNet-GMP的行人重识别方法 Person Re-identification Method Based on GoogLeNet-GMP Based on Vector Attention Mechanism 计算机科学, 2022, 49(7): 142-147. https://doi.org/10.11896/jsjkx.210600198
[4]	杜丽君, 唐玺璐, 周娇, 陈玉兰, 程建. 基于注意力机制和多任务学习的阿尔茨海默症分类 Alzheimer's Disease Classification Method Based on Attention Mechanism and Multi-task Learning 计算机科学, 2022, 49(6A): 60-65. https://doi.org/10.11896/jsjkx.201200072
[5]	陈永平, 朱建清, 谢懿, 吴含笑, 曾焕强. 基于外接圆半径差损失的实时安全帽检测算法 Real-time Helmet Detection Algorithm Based on Circumcircle Radius Difference Loss 计算机科学, 2022, 49(6A): 424-428. https://doi.org/10.11896/jsjkx.220100252
[6]	孙洁琪, 李亚峰, 张文博, 刘鹏辉. 基于离散小波变换的双域特征融合深度卷积神经网络 Dual-field Feature Fusion Deep Convolutional Neural Network Based on Discrete Wavelet Transformation 计算机科学, 2022, 49(6A): 434-440. https://doi.org/10.11896/jsjkx.210900199
[7]	郁舒昊, 周辉, 叶春杨, 王太正. SDFA:基于多特征融合的船舶轨迹聚类方法研究 SDFA:Study on Ship Trajectory Clustering Method Based on Multi-feature Fusion 计算机科学, 2022, 49(6A): 256-260. https://doi.org/10.11896/jsjkx.211100253
[8]	杨玥, 冯涛, 梁虹, 杨扬. 融合交叉注意力机制的图像任意风格迁移 Image Arbitrary Style Transfer via Criss-cross Attention 计算机科学, 2022, 49(6A): 345-352. https://doi.org/10.11896/jsjkx.210700236
[9]	蓝凌翔, 池明旻. 基于特征注意力融合网络的遥感变化检测研究 Remote Sensing Change Detection Based on Feature Fusion and Attention Network 计算机科学, 2022, 49(6): 193-198. https://doi.org/10.11896/jsjkx.210500058
[10]	李发光, 伊力哈木·亚尔买买提. 基于改进CenterNet的航拍绝缘子缺陷实时检测模型 Real-time Detection Model of Insulator Defect Based on Improved CenterNet 计算机科学, 2022, 49(5): 84-91. https://doi.org/10.11896/jsjkx.210400142
[11]	董奇达, 王喆, 吴松洋. 结合注意力机制与几何信息的特征融合框架 Feature Fusion Framework Combining Attention Mechanism and Geometric Information 计算机科学, 2022, 49(5): 129-134. https://doi.org/10.11896/jsjkx.210300180
[12]	李鹏祖, 李瑶, Ibegbu Nnamdi JULIAN, 孙超, 郭浩, 陈俊杰. 基于多特征融合的重叠组套索脑功能超网络构建及分类 Construction and Classification of Brain Function Hypernetwork Based on Overlapping Group Lasso with Multi-feature Fusion 计算机科学, 2022, 49(5): 206-211. https://doi.org/10.11896/jsjkx.210300049
[13]	范新南, 赵忠鑫, 严炜, 严锡君, 史朋飞. 结合注意力机制的多尺度特征融合图像去雾算法 Multi-scale Feature Fusion Image Dehazing Algorithm Combined with Attention Mechanism 计算机科学, 2022, 49(5): 50-57. https://doi.org/10.11896/jsjkx.210400093
[14]	高心悦, 田汉民. 基于改进U-Net网络的液滴分割方法 Droplet Segmentation Method Based on Improved U-Net Network 计算机科学, 2022, 49(4): 227-232. https://doi.org/10.11896/jsjkx.210300193
[15]	徐涛, 陈奕仁, 吕宗磊. 基于改进YOLOv3的机坪工作人员反光背心检测研究 Study on Reflective Vest Detection for Apron Workers Based on Improved YOLOv3 Algorithm 计算机科学, 2022, 49(4): 239-246. https://doi.org/10.11896/jsjkx.210200119

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed