基于多尺度多粒度特征的行人重识别

doi:10.11896/jsjkx.200600043

计算机科学 ›› 2021, Vol. 48 ›› Issue (7): 238-244.doi: 10.11896/jsjkx.200600043

• 计算机图形学&多媒体 • 上一篇下一篇

基于多尺度多粒度特征的行人重识别

王栋¹, 周大可^1,2, 黄有达¹, 杨欣¹

1 南京航空航天大学自动化学院南京211100
2 江苏省物联网与控制技术重点实验室(南京航空航天大学) 南京211100

收稿日期:2020-06-05 修回日期:2020-09-18 出版日期:2021-07-15 发布日期:2021-07-02
通讯作者: 周大可(dkzhou@nuaa.edu.cn)
基金资助:
国家自然科学基金(61573182)

Multi-scale Multi-granularity Feature for Pedestrian Re-identification

WANG Dong¹, ZHOU Da-ke^1,2, HUANG You-da¹ , YANG Xin¹

1 School of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211100,China
2 Jiangsu Key Laboratory of Internet of Things and Control Technologies (Nanjing University of Aeronautics and Astronautics),Nanjing 211100,China

Received:2020-06-05 Revised:2020-09-18 Online:2021-07-15 Published:2021-07-02
About author:WANG Dong,born in 1996,postgra-duate.His main research interests include target detection,pedestrian re-identification and target tracking.(m15150690108@163.com)
ZHOU Da-ke,born in 1974,Ph.D,associate professor.His main research in-terests include digital image processing,computer vision and machine learning.
Supported by:
National Natural Science Foundation of China(61573182).

摘要/Abstract

摘要： 针对现有的基于卷积神经网络的行人重识别方法所提取的特征辨识力不足的问题,提出了一种基于多尺度多粒度特征的行人重识别方法。在训练阶段,该方法在卷积神经网络的不同尺度提取特征;然后对获得的多尺度特征图进行分块和池化,从而得到不同尺度的全局特征和局部特征的多粒度特征,使用不确定性权重调节Softmax损失和三元组损失来对特征向量进行监督训练。在推理阶段,对所获得的多尺度多粒度的特征进行融合,使用融合特征在图像库中进行相似度匹配。在Market-1501和DukeMTMC-ReID数据集上的实验表明,所提方法相比基准网络ResNet-50在Rank-1评价指标上分别提升了4.3%和3.6%,在mAP评价指标上分别提升了6.2%和6.6%。实验结果表明,所提方法能够增强提取特征的辨识力,提高行人重识别的性能。

关键词: 多尺度特征, 多粒度特征, 机器视觉, 卷积神经网络, 行人重识别

Abstract: In order to address the problem of insufficient discriminative features for pedestrian re-identification extracted by exis-ting convolutional neural network,a novel multi-scale multi-granularity feature learning for pedestrian re-identification method is proposed.In the training phase,the method extracts multi-scale features at different stages of the convolutional neural network,and then blocks and pools these feature maps to obtain multi-granularity features containing global and local features,uses uncertainty to weight Softmax loss and triples loss and to supervise training process on feature vectors.In the inference phase,the obtained multi-scale multi-granularity features are concatenated,and finally the concatenated features are used to perform similarity matching in the gallery.Experiments on the Market-1501 and DukeMTMC-ReID datasets show that the proposed method improves the Rank-1 evaluation index by 4.3% and 3.6%,respectively,compared with the benchmark network ResNet-50,and improves the mAP evaluation index respectively 6.2% and 6.6%.The results show that the proposed method can enhance the discrimination of extracted features and improve the performance of pedestrian re-identification.

Key words: Convolutional neural network, Machine vision, Multi-granularity features, Multi-scale features, Pedestrian re-identification

中图分类号:

TP399

王栋, 周大可, 黄有达, 杨欣. 基于多尺度多粒度特征的行人重识别[J]. 计算机科学, 2021, 48(7): 238-244. https://doi.org/10.11896/jsjkx.200600043

WANG Dong, ZHOU Da-ke, HUANG You-da , YANG Xin. Multi-scale Multi-granularity Feature for Pedestrian Re-identification[J]. Computer Science, 2021, 48(7): 238-244. https://doi.org/10.11896/jsjkx.200600043

参考文献

[1]LUO H,JIANG W,FAN X,et al.A Survey on Deep Learning Based Person Re-identification[J].Acta Automatica Sinic,2019,45(11):2032-2049.
[2]MARTIN K,HIRZER M,WOHLHART P,et al.Large ScaleMetric Learning from Equivalence Constraints[C]//IEEE Conference on Computer Vision and Pattern Recognition.Providence:IEEE Press,2012:2288-2295.
[3]LIAO S,HU Y,ZHU X,et al.Person re-identification by local maximal occurrence representation and metric learning[C]//IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE Press,2015:2197-2206.
[4]HUAN X Y,XU J L,GUO G,et al.Real-time pedestrian re-reco-gnition based on enhanced aggregate channel features[J].Progress in Laser and Optoelectronics,2017(9):119-127.
[5]ZHENG L,YANG Y,HAUPTMANN A G.Person re-identification:Past,present and future [J].arXiv:1610.02984.
[6]HERMANS A,BEYER L,LEIBE B.In Defense of the Triplet Loss for Person Re-Identification[J].arXiv:1703.07737.
[7]ZHANG G N,WANG J B,ZHANG Y F,et al.Pedestrian recognition method based on feature fusion[J].Computer Engineering and Applications,2017(12):190-194,245.
[8]WEI L,ZHANG S,YAO H,et al.GLAD:Global-Local-Alignment Descriptor for Pedestrian Retrieval[C]//Proceedings of the 25th ACM international conference on Multimedia.New York:ACM Press,2017:420-428.
[9]SU C,LI J,ZHANG S,et al.Pose-driven Deep Convolutional Model for Person Re-identification[C]//IEEE Conference on Computer Vision and Pattern Recognition.Hawaii:IEEE Press,2017:3980-3989.
[10]ZHAO H,TIAN M,SUN S,et al.Spindle Net:Person Re-identification with Human Body Region Guided Feature Decomposition and Fusion[C]//IEEE Conference on Computer Vision and Pattern Recognition.Hawaii:IEEE Press,2017:1077-1085.
[11]SUN Y,ZHENG L,YANG Y,et al.Beyond Part Models:Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)[C]//European Conference on Computer Vision.Munich:IEEE Press,2018:480-496.
[12]LIU Z Y,WAN P P.Feature extraction method for pedestrian re-recognition based on attention mechanism[J].Journal of Computer Applications,2020,40(3):672-676.
[13]LI W,ZHU X,GONG S.Harmonious Attention Network for Person Re-Identification[C]//IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE Press,2018:2285-2294.
[14]SI J,ZHANG H,LI C G,et al.Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification[C]//IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE Press,2018:5363-5372.
[15]ZHONG Z,ZHENG L,ZHENG Z D,et al.CamStyle:A Novel Data Augmentation Method for Person Re-Identification[J].IEEE Transactions on Image Processing,2019,28(3):1176-1190.
[16]ZHANG S L,CAO X.Improved Person Re-Identification Algorithm on Camstyle [J].CEA,2020,56(15):124-131.
[17]ZHONG Z,ZHENG L,KANG G,et al.Random erasing dataaugmentation [J].arXiv:1708.04896.
[18]ZHONG Z,ZHENG L,CAO D,et al.Re-ranking Person Re-identification with k-reciprocal Encoding[C]//IEEE Conference on Computer Vision and Pattern Recognition.Hawaii:IEEE Press,2017:3652-3661.
[19]HE K,ZHANG X,REN S,et al.Deep Residual Learning forImage Recognition [C]//IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE Press,2016:770-778.
[20]LIN T Y,DOLLAR P,GIRSHICK R,et al.Feature pyramidnetworks for object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition.Honolulu:IEEE Press,2017:2117-2125.
[21]CHRISTIAN S,VINCENT V,SERGER I,et al.Rethinking the inception architecture for computer vision[C]//IEEEConfe-rence on Computer Vision and Pattern Recognition.Las Vegas:IEEE Press,2016:2818-2826.
[22]CIPOLLA R,GAL Y,KENDALL A.Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE Press,2018:7482-7491.
[23]ZHENG L,SHEN L Y,TIAN L,et al.Scalable Person Re-identification:A Benchmark[C]//The IEEE InternationalConfe-rence on Computer Vision.Santiago:IEEE Press,2015:1116-1124.
[24]ZHANG X,LUO H,FAN X,et al.Alignedreid:Surpassing human-level performance in person reidentification[J].arXiv:1711.08184.
[25]TAY C,ROY S,YAP K.AANet:Attribute Attention Network for Person Re-Identifications[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE Press,2019:7127-7136.
[26]HOU R B,MA B P,CHANG H,et al.Interaction-and-Aggregation Network for Person Re-identification [C]//The IEEE Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE Press,2019:9317-9326.
[27]LUO H,JIANG W,GU Y,et al.Bag of Tricks and A Strong Baseline for Deep Person Re-identification [C]//The IEEE Conference on Computer Vision and Pattern Recognition Workshops.Long Beach:IEEE Press,2019:4321-4329.
[28]ZHOU K,YANG Y,CAVALLARO A.et al.Omni-Scale Feature Learning for Person Re-Identification[C]//2019 IEEE/CVF International Conference on Computer Vision.Seoul:IEEE Press,2019:3701-3711.

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基于多尺度多粒度特征的行人重识别

Multi-scale Multi-granularity Feature for Pedestrian Re-identification

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