Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (3): 170-178.doi: 10.11896/jsjkx.210100132

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Person Re-identification Based on Feature Location and Fusion

YANG Xiao-yu1, YIN Kang-ning1, HOU Shao-qi2, DU Wen-yi1, YIN Guang-qiang1   

  1. 1 School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610000,China
    2 School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 610000,China
  • Received:2021-01-18 Revised:2021-04-16 Online:2022-03-15 Published:2022-03-15
  • About author:YANG Xiao-yu,born in 1996,postgra-duate.His main research interests include deep learning and computer vision.
    YIN Guang-qiang,born in 1982,professor.His main research interests include network security,computer vision,signal processing and intelligent manufacturing.

PDF (PC)

683

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

CLC Number: 

  • TP399
[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.
[1] CHEN Kun-feng, PAN Zhi-song, WANG Jia-bao, SHI Lei, ZHANG Jin. Moderate Clothes-Changing Person Re-identification Based on Bionics of Binocular Summation [J]. Computer Science, 2022, 49(8): 165-171.
[2] ZHANG Ying-tao, ZHANG Jie, ZHANG Rui, ZHANG Wen-qiang. Photorealistic Style Transfer Guided by Global Information [J]. Computer Science, 2022, 49(7): 100-105.
[3] CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126.
[4] MENG Yue-bo, MU Si-rong, LIU Guang-hui, XU Sheng-jun, HAN Jiu-qiang. Person Re-identification Method Based on GoogLeNet-GMP Based on Vector Attention Mechanism [J]. Computer Science, 2022, 49(7): 142-147.
[5] DU Li-jun, TANG Xi-lu, ZHOU Jiao, CHEN Yu-lan, CHENG Jian. Alzheimer's Disease Classification Method Based on Attention Mechanism and Multi-task Learning [J]. Computer Science, 2022, 49(6A): 60-65.
[6] CHEN Yong-ping, ZHU Jian-qing, XIE Yi, WU Han-xiao, ZENG Huan-qiang. Real-time Helmet Detection Algorithm Based on Circumcircle Radius Difference Loss [J]. Computer Science, 2022, 49(6A): 424-428.
[7] SUN Jie-qi, LI Ya-feng, ZHANG Wen-bo, LIU Peng-hui. Dual-field Feature Fusion Deep Convolutional Neural Network Based on Discrete Wavelet Transformation [J]. Computer Science, 2022, 49(6A): 434-440.
[8] YU Shu-hao, ZHOU Hui, YE Chun-yang, WANG Tai-zheng. SDFA:Study on Ship Trajectory Clustering Method Based on Multi-feature Fusion [J]. Computer Science, 2022, 49(6A): 256-260.
[9] YANG Yue, FENG Tao, LIANG Hong, YANG Yang. Image Arbitrary Style Transfer via Criss-cross Attention [J]. Computer Science, 2022, 49(6A): 345-352.
[10] LAN Ling-xiang, CHI Ming-min. Remote Sensing Change Detection Based on Feature Fusion and Attention Network [J]. Computer Science, 2022, 49(6): 193-198.
[11] LI Fa-guang, YILIHAMU·Yaermaimaiti. Real-time Detection Model of Insulator Defect Based on Improved CenterNet [J]. Computer Science, 2022, 49(5): 84-91.
[12] DONG Qi-da, WANG Zhe, WU Song-yang. Feature Fusion Framework Combining Attention Mechanism and Geometric Information [J]. Computer Science, 2022, 49(5): 129-134.
[13] LI Peng-zu, LI Yao, Ibegbu Nnamdi JULIAN, SUN Chao, GUO Hao, CHEN Jun-jie. Construction and Classification of Brain Function Hypernetwork Based on Overlapping Group Lasso with Multi-feature Fusion [J]. Computer Science, 2022, 49(5): 206-211.
[14] FAN Xin-nan, ZHAO Zhong-xin, YAN Wei, YAN Xi-jun, SHI Peng-fei. Multi-scale Feature Fusion Image Dehazing Algorithm Combined with Attention Mechanism [J]. Computer Science, 2022, 49(5): 50-57.
[15] GAO Xin-yue, TIAN Han-min. Droplet Segmentation Method Based on Improved U-Net Network [J]. Computer Science, 2022, 49(4): 227-232.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.