Computer Science ›› 2022, Vol. 49 ›› Issue (8): 165-171.doi: 10.11896/jsjkx.210600140

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Moderate Clothes-Changing Person Re-identification Based on Bionics of Binocular Summation

CHEN Kun-feng, PAN Zhi-song, WANG Jia-bao, SHI Lei, ZHANG Jin   

  1. College of Command and Control Engineering,Army Engineering University of PLA,Nanjing 210007,China
  • Received:2021-06-17 Revised:2021-10-19 Published:2022-08-02
  • About author:CHEN Kun-feng,born in 1995,postgraduate.His main research interests include computer vision and person re-identification.
    PAN Zhi-song,born in 1973,Ph.D,professor.His main research interests include pattern recognition and machine learning.
  • Supported by:
    National Natural Science Foundation of China(62076251) and Natural Science Foundation of Jiangsu Province(BK20200581).

Abstract: Moderate clothes-changing person re-identification is to find the same person from different camera scenes under the premise of considering the moderate change of clothes.The implementation of existing person re-identification methods is usually based on the assumption that the pedestrian’s clothing is invariant,so they rely on clothing-related features.Then,when the above assumptions are not valid,these methods are difficult to achieve the ideal recognition performance.Considering the important characteristic that pedestrian’s shape hardly change when the change of clothes is moderate,the moderate clothes-changing person re-identification is studied.Inspired by the binocular summation in biological vision system,a self-attention siamese network is proposed in this paper.Analogous to biological binocular information acquisition process,the network takes two types of images of the same pedestrian with different clothes as two branch inputs,and then achieves summation effect with siamese architecture.Subsequently,the contrastive learning and fusion learning of multiple features are carried out to obtain the pedestrian feature representation with identity discrimination.Finally,empirical studies show that the proposed method achieves best performance at present on clothes-changing person re-identification benchmark.

Key words: Binocular summation, Moderate clothes-changing, Person re-identification, Self-attention, Siamese network

CLC Number: 

  • TP391
[1]GHEISSARI N,SEBASTIAN T B,HARTLEY R.Person reidentification using spatiotemporal appearance[C]//2006 IEEEComputer Society Conference on Computer Vision and Pattern Recognition(CVPR’06).IEEE,2006:1528-1535.
[2]FARENZENA M,BAZZANI L,PERINA A,et al.Person re-identification by symmetry-driven accumulation of local features[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.IEEE,2010:2360-2367.
[3]GRAY D,TAO H.Viewpoint invariant pedestrian recognition with an ensemble of localized features[C]//European Confe-rence on Computer Vision.Berlin:Springer,2008:262-275.
[4]HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[5]PAISITKRIANGKRAI S,SHEN C,VAN DEN HENGEL A.Learning to rank in person re-identification with metric ensembles[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:1846-1855.
[6]SHEN Y,XIAO T,LI H,et al.End-to-end deep kronecker-pro-duct matching for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:6886-6895.
[7]ZHENG Z,YANG X,YU Z,et al.Joint discriminative and gene-rative learning for person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:2138-2147.
[8]WEI L,ZHANG S,GAO W,et al.Person transfer gan to bridge domain gap for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:79-88.
[9]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.Cham:Springer,2016:17-35.
[10]ZHENG L,SHEN L,TIAN L,et al.Scalable person re-identification:A benchmark[C]//Proceedings of the IEEE InternationalConference on Computer Vision.2015:1116-1124.
[11]HOU R,CHANG H,MA B,et al.Temporal complementarylearning for video person re-identification[C]//European Conference on Computer Vision.Cham:Springer,2020:388-405.
[12]FAN C,PENG Y,CAO C,et al.Gaitpart:Temporal part-based model for gait recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:14225-14233.
[13]LORENZO-NAVARRO J,CASTRILLÓN-SANTANA M,HERNÁNDEZ-SOSA D.An study on re-identification in RGB-D imagery[C]//International Workshop on Ambient Assisted Li-ving.Berlin:Springer,2012:200-207.
[14]HAQUE A,ALAHI A,FEI-FEI LI F F.Recurrent attentionmodels for depth-based person identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:1229-1238.
[15]WU A,ZHENG W S,LAI J H.Robust depth-based person re-identification[J].IEEE Transactions on Image Processing,2017,26(6):2588-2603.
[16]JIA X,MENG Z,KATIPALLY K,et al.Clothing ChangeAware Person Identification[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).IEEE Computer Society,2018.
[17]YANG Q Z,WU A C,ZHENG W S.Person Re-identification by Contour Sketch under Moderate Clothing Change[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2021,3(6):2029-2046.
[18]LI Y J,LUO Z Y,WENG X S,et al.Learning shape representations for clothing variations in person re-identification[J].ar-Xiv:2003.07340,2020.
[19]ZHANG J,LI Y,CHEN F Q,et al.X-Net:A Binocular Summation Network for Foreground Segmentation[J].IEEE Access,2019,7:1412-71422.
[20]BLAKE R,WILSON H.Binocular Vision[J].Vision Research,2011,51(7):754-770.
[21]LOWE D G.Distinctive Image Features from Scale-InvariantKeypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[22]REDMON J,FARHADI A.Yolov3:An incremental improve-ment[J].arXiv:1804.02767,2018.
[23]XIE S,TU Z.Holistically-nested edge detection[C]//Procee-dings of the IEEE International Conference on Computer Vision.2015:1395-1403.
[24]HU J,LI S,ALBANIE S.Squeeze-and-Excitation Networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2018.
[25]YE M,SHEN J,LIN G,et al.Deep learning for person re-identification:A survey and outlook[J/OL].IEEE Transactions on Pattern Analysis and Machine Intelligence,2021.
[26]XIA B N,GONG Y,ZHANG Y,et al.Second-order non-local attention networks for person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:3760-3769.
[27]YU F,KOLTUN V.Multi-scale context aggregation by dilated convolutions[J].arXiv:1511.07122,2015.
[28]OJALA T,PIETIKÄINEN M,HARWOOD D.A comparative study of texture measures with classification based on featured distributions[J].Pattern Recognition,1996,29(1):51-59.
[29]DALAL N,TRIGGS B.Histograms of oriented gradients forhuman detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR’05).IEEE,2005:886-893.
[30]KOESTINGER M,HIRZER M,WOHLHART P,et al.Large scale metric learning from equivalence constraints[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2012:2288-2295.
[31]SUN Y,ZHENG L,YANG Y,et al.Beyond part models:Person retrieval with refined part pooling (and a strong convolutional baseline)[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:480-496.
[1] WU Zi-yi, LI Shao-mei, JIANG Meng-han, ZHANG Jian-peng. Ontology Alignment Method Based on Self-attention [J]. Computer Science, 2022, 49(9): 215-220.
[2] FANG Yi-qiu, ZHANG Zhen-kun, GE Jun-wei. Cross-domain Recommendation Algorithm Based on Self-attention Mechanism and Transfer Learning [J]. Computer Science, 2022, 49(8): 70-77.
[3] JIN Fang-yan, WANG Xiu-li. Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM [J]. Computer Science, 2022, 49(7): 179-186.
[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] ZHANG Jia-hao, LIU Feng, QI Jia-yin. Lightweight Micro-expression Recognition Architecture Based on Bottleneck Transformer [J]. Computer Science, 2022, 49(6A): 370-377.
[6] ZHAO Dan-dan, HUANG De-gen, MENG Jia-na, DONG Yu, ZHANG Pan. Chinese Entity Relations Classification Based on BERT-GRU-ATT [J]. Computer Science, 2022, 49(6): 319-325.
[7] HAN Jie, CHEN Jun-fen, LI Yan, ZHAN Ze-cong. Self-supervised Deep Clustering Algorithm Based on Self-attention [J]. Computer Science, 2022, 49(3): 134-143.
[8] ZHAO Yue, YU Zhi-bin, LI Yong-chun. Cross-attention Guided Siamese Network Object Tracking Algorithm [J]. Computer Science, 2022, 49(3): 163-169.
[9] 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.
[10] HU Yan-li, TONG Tan-qian, ZHANG Xiao-yu, PENG Juan. Self-attention-based BGRU and CNN for Sentiment Analysis [J]. Computer Science, 2022, 49(1): 252-258.
[11] ZHANG Xin-feng, SONG Bo. A Person Re-identification Method Based on Improved Triple Loss and Feature Fusion [J]. Computer Science, 2021, 48(9): 146-152.
[12] HU De-feng, ZHANG Chen-xi, WANG Shi-tao, ZHAO Qin-pei, LI Jiang-feng. Intelligent Prediction Model of Tool Wear Based on Deep Signal Processing and Stacked-ResGRU [J]. Computer Science, 2021, 48(6): 175-183.
[13] CHENG Xu, CUI Yi-ping, SONG Chen, CHEN Bei-jing, ZHENG Yu-hui, SHI Jin-gang. Object Tracking Algorithm Based on Temporal-Spatial Attention Mechanism [J]. Computer Science, 2021, 48(4): 123-129.
[14] WANG Xi, ZHANG Kai, LI Jun-hui, KONG Fang, ZHANG Yi-tian. Generation of Image Caption of Joint Self-attention and Recurrent Neural Network [J]. Computer Science, 2021, 48(4): 157-163.
[15] DONG Hu-sheng, ZHONG Shan, YANG Yuan-feng, SUN Xun, GONG Sheng-rong. Person Re-identification by Region Correlated Deep Feature Learning with Multiple Granularities [J]. Computer Science, 2021, 48(12): 269-277.
Full text



No Suggested Reading articles found!