Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 2/2023

18.11.2022 | Theoretical Advances

Occluded person re-identification based on embedded graph matching network for contrastive feature relation

verfasst von: Shuren Zhou, Mengsi Zhang

Erschienen in: Pattern Analysis and Applications | Ausgabe 2/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The main goal of person re-identification (ReID) is to identify human images captured by different cameras. However, people are often occluded by various obstacles. To solve the occlusion problem, this paper proposes a novel method that occluded person re-identification based on embedded graph matching network for contrastive feature relation. It involves three modules. In the contrast feature construction (C) module, we use the trained pose estimation (PE) model to extract the local features shown by the key points of person image. In addition, we propose the global discrepant contrastive pooling (GDCP) layer to capture global features after noise removal. At this point, the extracted features are only the node feature information,and what is not connected is a single one. In the adaptive node relationship (R) module, the node relation learning (NRL) layer is proposed to establish the contrast feature relationship between the individual information of feature extraction and the joint structure of monitoring the key points of the feature relationship, so as to automatically update the relationship between features, promote the transmission of meaningful features and inhibit the transmission of meaningless features. In this way, the relationship between the feature information can also be extracted. In the embedded graph matching (M) module, the embedded feature alignment (EFA) layer is proposed to directly predict the similarity score of each feature of the two images. In addition, the information collected from the non-occluded part of one image is used to compensate the occluded part corresponding to the other image, which reduces the dispersion of image information. We avoid using a sensitive one-to-one hard alignment instead of many-to-many soft alignment to enhance robustness. On Occluded_Duke, DukeMTMC and Market1501,this method is superior to the existing person re-identification methods. Particularly on Occluded_Duke, the Rank-1 reaches 60.5%, and mAP reaches 49.2%.
Vorheriger Artikel A large-scale performance study of entropy-based image thresholding techniques using new SAD metric
Nächster Artikel On error reduction by the symmetric rejection method in multi-stage biometric verification systems

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, Tacchetti A, Raposo D, Santoro A, Faulkner R, et al (2018) Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:​1806.​01261
2.
Bhuiyan A, Liu Y, Siva P, Javan M, Ayed IB, Granger E (2020) Pose guided gated fusion for person re-identification. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp 2675–2684
3.
Chen P, Liu W, Dai P, Liu J, Ye Q, Xu M, Chen Q, Ji R (2021) Occlude them all: occlusion-aware attention network for occluded person re-id. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 11833–11842
4.
Cho Y-J, Yoon K-J (2018) Pamm: Pose-aware multi-shot matching for improving person re-identification. IEEE Trans Image Process 27(8):3739–3752MathSciNetCrossRefMATH
5.
Eom C, Ham B (2019) Learning disentangled representation for robust person re-identification. Adv Neural Inf Process Syst 32
6.
Yang F, Wei Y, Zhou Y, Shi H, Huang G, Wang X, Yao Z, Huang T (2019) Horizontal pyramid matching for person re-identification. In: Proceedings of the AAAI conference on artificial intelligence vol 33, pp 8295–8302
7.
Gao F, Jin Y, Ge Y, Lu S, Zhang Y (2020) Occluded person re-identification based on feature fusion and sparse reconstruction. Multimedi Tools Appl, pp 1–18
8.
Gao S, Wang J, Lu H, Liu Z (2020) Pose-guided visible part matching for occluded person reid. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 11744–11752
9.
He L, Liang J, Li H, Sun Z (2018) Deep spatial feature reconstruction for partial person re-identification: alignment-free approach. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7073–7082
10.
He L, Liu W (2020) Guided saliency feature learning for person re-identification in crowded scenes. In: European conference on computer vision, pp 357–373. Springer
11.
12.
He L, Wang Y, Liu W, Zhao H, Sun Z, Feng J (2019) Foreground-aware pyramid reconstruction for alignment-free occluded person re-identification. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 8450–8459
13.
Herzog F, Ji X, Teepe T, Hörmann S, Gilg J, Rigoll G (2021) Lightweight multi-branch network for person re-identification. arXiv preprint arXiv:​2101.​10774
14.
Hou R, Ma B, Chang H, Gu X, Shan S, Chen X (2019) Vrstc: occlusion-free video person re-identification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 7183–7192
15.
Huang H, Chen X, Huang K (2020) Human parsing based alignment with multi-task learning for occluded person re-identification. In: 2020 IEEE international conference on multimedia and expo (ICME), pp 1–6. IEEE
16.
Huang H, Li D, Zhang Z, Chen X, Huang K (2018) Adversarially occluded samples for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5098–5107
17.
Iodice S, Mikolajczyk K (2018) Partial person re-identification with alignment and hallucination. In: Asian conference on computer vision, pp 101–116. Springer
18.
Isobe T, Li D, Tian L, Chen W, Shan Y, Wang S (2021) Towards discriminative representation learning for unsupervised person re-identification. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 8526–8536
19.
Jin X, Lan C, Zeng W, Chen Z (2020) Global distance-distributions separation for unsupervised person re-identification. In: European conference on computer vision, pp 735–751. Springer
20.
Jin X, Lan C, Zeng W, Chen Z, Zhang L (2020) Style normalization and restitution for generalizable person re-identification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 3143–3152
21.
Kalayeh MM, Basaran E, Gökmen M, Kamasak ME, Shah M (2018) Human semantic parsing for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1062–1071
22.
Khatun A, Denman S, Sridharan S, Fookes C (2020) Joint identification-verification for person re-identification: a four stream deep learning approach with improved quartet loss function. Comput Vis Image Underst 197:102989CrossRef
23.
Li W, Zhu X, Gong S (2018) Harmonious attention network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2285–2294
24.
Lin Y, Zheng L, Zheng Z, Wu Y, Hu Z, Yan C, Yang Y (2019) Improving person re-identification by attribute and identity learning. Pattern Recogn 95:151–161CrossRef
25.
Liu J, Ni B, Yan Y, Zhou P, Cheng S, Hu J (2018) Pose transferrable person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4099–4108
26.
Luo H, Gu Y, Liao X, Lai S, Jiang W (2019) Bag of tricks and a strong baseline for deep person re-identification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops
27.
Luo H, Jiang W, Fan X, Zhang C (2020) Stnreid: Deep convolutional networks with pairwise spatial transformer networks for partial person re-identification. IEEE Trans Multimed 22(11):2905–2913CrossRef
28.
Luo H, Jiang W, Zhang X, Fan X, Qian J, Zhang C (2019) Alignedreid++: dynamically matching local information for person re-identification. Pattern Recogn 94:53–61CrossRef
29.
Ma L, Jia X, Sun Q, Schiele B, Tuytelaars T, Van Gool L (2017) Pose guided person image generation. Adv Neural Inf Process Syst 30
30.
Miao J, Wu Yu, Liu P, Ding Y, Yang Y (2019) Pose-guided feature alignment for occluded person re-identification. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 542–551
31.
Park H, Ham B (2020) Relation network for person re-identification. In: Proceedings of the AAAI conference on artificial intelligence vol 34, pp 11839–11847
32.
Qi L, Huo J, Wang L, Shi Y, Gao Y (2018) Maskreid: a mask based deep ranking neural network for person re-identification. arXiv preprint arXiv:​1804.​03864D
33.
Sarfraz MS, Schumann A, Eberle A, Stiefelhagen R (2018) A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 420–429
34.
Shu X, Li G, Wei L, Zhong J-X, Zang X, Zhang S, Wang Y, Liang Y, Tian Q (2021) Diverse part attentive network for video-based person re-identification. Pattern Recogn Lett
35.
Song C, Huang Y, Ouyang W, Wang L (2018) Mask-guided contrastive attention model for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1179–1188
36.
Subramaniam A, Nambiar A, Mittal A (2019) Co-segmentation inspired attention networks for video-based person re-identification. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 562–572
37.
Sun K, Xiao B, Liu D, Wang J (2019) Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 5693–5703
38.
Sun Y, Xu Q, Li Y, Zhang C, Li Y, Wang S, Sun J (2019) Perceive where to focus: learning visibility-aware part-level features for partial person re-identification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 393–402
39.
Sun Y, Zheng L, Yang Y, Tian Q, Wang S (2018) Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In: Proceedings of the European conference on computer vision (ECCV), pp 480–496
40.
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)
41.
Tagore NK, Chattopadhyay P (2020) Smsnet: a novel multi-scale siamese model for person re-identification. In ICETE 1:103–112
42.
Wang G, Yang S, Liu H, Wang Z, Yang Y, Wang S, Yu G, Zhou E, Sun J (2020) High-order information matters: learning relation and topology for occluded person re-identification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 6449–6458
43.
Wang G, Lai J-H, Liang W, Wang G (2020) Smoothing adversarial domain attack and p-memory reconsolidation for cross-domain person re-identification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10568–10577
44.
Wang G, Yuan Y, Chen X, Li J, Zhou X (2018) Learning discriminative features with multiple granularities for person re-identification. In: Proceedings of the 26th ACM international conference on multimedia, pp 274–282
45.
Wang H, Wang G, Li Y, Zhang D, Lin L (2020) Transferable, controllable, and inconspicuous adversarial attacks on person re-identification with deep mis-ranking. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 342–351
46.
Wang R, Yan J, Yang X (2019) Learning combinatorial embedding networks for deep graph matching. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 3056–3065
47.
Ye M, Shen J, Crandall DJ, Shao L, Luo J (2020) Dynamic dual-attentive aggregation learning for visible-infrared person re-identification. In: Computer vision–ECCV 2020: 16th European conference, glasgow, UK, August 23–28, 2020, Proceedings, Part XVII 16, pp 229–247. Springer
48.
Zanfir A, Sminchisescu C (2018) Deep learning of graph matching. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2684–2693
49.
Zhang X, Luo H, Fan X, Xiang W, Sun Y, Xiao Q, Jiang W, Zhang C, Sun J (2017) Alignedreid: surpassing human-level performance in person re-identification. arXiv preprint arXiv:​1711.​08184
50.
Zhang Z, Xie Y, Li D, Zhang W, Tian Q (2020) Learning to align via wasserstein for person re-identification. IEEE Trans Image Process 29:7104–7116CrossRefMATH
51.
Zhao C, Lv X, Dou S, Zhang S, Jun W, Wang L (2021) Incremental generative occlusion adversarial suppression network for person reid. IEEE Trans Image Process 30:4212–4224CrossRef
52.
Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: a benchmark. In: Proceedings of the IEEE international conference on computer vision, pp 1116–1124
53.
Zhu K, Guo H, Liu Z, Tang M, Wang J (2020) Identity-guided human semantic parsing for person re-identification. In: Proceedings of computer vision–ECCV 2020: 16th European conference, Glasgow, UK, August 23–28, 2020, Part III 16, pp 346–363. Springer
54.
Zhuo J, Chen Z, Lai J, Wang G (2018) Occluded person re-identification. In: 2018 IEEE international conference on multimedia and expo (ICME), pp 1–6. IEEE
55.
Zhuo J, Lai J, Chen P (2019) A novel teacher-student learning framework for occluded person re-identification. arXiv preprint arXiv:​1907.​03253
56.
Ullah KS, Ul HI, Noman K, Khan M, Mohammad H, Wook BS (2022) Learning to rank: an intelligent system for person reidentification. Int J Intell Syst 37(9):5924–5948CrossRef
Metadaten
Titel
Occluded person re-identification based on embedded graph matching network for contrastive feature relation
verfasst von
Shuren Zhou
Mengsi Zhang
Publikationsdatum
18.11.2022
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 2/2023
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-022-01123-x

Weitere Artikel der Ausgabe 2/2023

Pattern Analysis and Applications 2/2023 Zur Ausgabe

Theoretical Advances

Fuzzy and non-fuzzy k-quantile clustering for high-variance data

Short Paper

Parkinsonian gait patterns quantification from principal geodesic analysis

Short Paper

EEG-based emotion recognition with cascaded convolutional recurrent neural networks

Short Paper

Event prediction with rough-fuzzy sets

Theoretical Advances

Improved swarm-optimization-based filter-wrapper gene selection from microarray data for gene expression tumor classification

Short Paper

Semi-supervised transformable architecture search for feature distillation

Premium Partner

    Bildnachweise