article

People tracking in multi-camera systems: a review

Authors:

Rabah Iguernaissi,

Kheireddine Aziz,

Pierre DrapAuthors Info & Claims

Multimedia Tools and Applications, Volume 78, Issue 8

Pages 10773 - 10793

https://doi.org/10.1007/s11042-018-6638-5

Published: 01 April 2019 Publication History

Abstract

Ubiquitousness of multiple cameras in surveillance systems is very beneficial for studying peoples behavior. The multiple views of the observed scene permit the management of dynamic occlusions and failures that may affect any sensor. The multi-camera tracking of objects is considered as the basic step in the design of intelligent surveillance applications. This thematic had been addressed in several researches. Various methods had been proposed to achieve an accurate tracking in the most challenging conditions as occlusions and lighting variations. These methods are addressed in two main research lines: the centralized and the distributed tracking approaches. In this paper, we propose an overview of the multi-camera tracking of objects which summarizes and classifies the most used existing methods.

References

[1]

Alahi A, Vandergheynst P, Bierlaire M, Kunt M (2010) Cascade of descriptors to detect and track objects across any network of cameras. Comput Vis Image Underst 114(6):624---640

Digital Library

[2]

Arsic D, Hristov E, Lehment NH, Hörnler B, Schuller B, Rigoll G (2008) Applying multi layer homography for multi camera person tracking. In: ICDSC. IEEE, pp 1---9

[3]

Avraham T, Gurvich I, Lindenbaum M, Markovitch S (2012) Learning implicit transfer for person re-identification. In: Fusiello A, Murino V, Cucchiara R (eds) ECCV Workshops (1), springer, lecture notes in computer science, vol 7583, pp 381---390

Digital Library

[4]

Aziz KE, Merad D, Fertil B (2011) People re-identification across multiple non-overlapping cameras system by appearance classification and silhouette part segmentation. In: AVSS. IEEE Computer Society, pp 303---308

Digital Library

[5]

Bai S, Bai X, Tian Q (2017) Scalable person re-identification on supervised smoothed manifold. In: CVPR, vol 6, pp 7

[6]

Bak S, Corvée E, Brémond F, Thonnat M (2010a) Person re-identification using haar-based and dcd-based signature. In: AVSS. IEEE Computer Society, pp 1---8

Digital Library

[7]

Bak S, Corvée E, Brémond F, Thonnat M (2010b) Person re-identification using spatial covariance regions of human body parts. In: AVSS. IEEE Computer Society, pp 435---440

Digital Library

[8]

Bak S, Charpiat G, Corvee E, Bremond F, Thonnat M (2012) Learning to Match Appearances by Correlations in a Covariance Metric Space. In: Fitzgibbon A, Lazebnik S, Perona P, Sato Y, Schmid C (eds) 12th European Conference on Computer Vision. https://hal.inria.fr/hal-00731792, vol 7574. Springer, Florence, pp 806---820

Digital Library

[9]

Baltieri D, Vezzani R, Cucchiara R (2010) 3d body model construction and matching for real time people re-identification. In: Puppo E, brogni A, Floriani LD (eds) Eurographics Italian Chapter Conference, Eurographics, pp 65---71

[10]

Baltieri D, Vezzani R, Cucchiara R (2011a) 3dpes: 3d people dataset for surveillance and forensics. In: Proceedings of the 1st International ACM Workshop on Multimedia access to 3D Human Objects, Scottsdale, pp 59---64

Digital Library

[11]

Baltieri D, Vezzani R, Cucchiara R, Utasi A, Benedek C, Szirányi T (2011b) Multi-view people surveillance using 3d information. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). IEEE, pp 1817---1824

[12]

Bazzani L, Cristani M, Perina A, Murino V (2012) Multiple-shot person re-identification by chromatic and epitomic analyses. Pattern Recogn Lett 33(7):898---903

Digital Library

[13]

Bhuvana VP, Schranz M, Regazzoni CS, Rinner B, Tonello AM, Huemer M (2016) Multi-camera object tracking using surprisal observations in visual sensor networks. EURASIP J Adv Signal Process 2016(1):1---14

[14]

Bouma H, Borsboom S, den Hollander RJ, Landsmeer SH, Worring M (2012) Re-identification of persons in multi-camera surveillance under varying viewpoints and illumination. In: SPIE Defense, Security, and Sensing. International Society for Optics and Photonics, pp 83,590Q---83,590Q

[15]

Bredereck M, Jiang X, Körner M, Denzler J (2012) Data association for multi-object tracking-by-detection in multi-camera networks. In: 2012 Sixth International Conference on Distributed smart cameras (ICDSC). IEEE, pp 1---6

[16]

Brendel W, Amer M, Todorovic S (2011) Multiobject tracking as maximum weight independent set. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1273---1280.

Digital Library

[17]

Caviar:context aware vision using image-based active recognition (2004) http://homepages.inf.ed.ac.uk/rbf/CAVIAR/

[18]

del Blanco CR, Mohedano R, García NN, Salgado L, Jaureguizar F (2008) Color-based 3d particle filtering for robust tracking in heterogeneous environments. In: ICDSC. IEEE, pp 1---10

[19]

Chang X, Ma Z, Yang Y, Zeng Z, Hauptmann AG (2017a) Bi-level semantic representation analysis for multimedia event detection. IEEE Trans Cybern 47(5):1180---1197

[20]

Chang X, Yu YL, Yang Y, Xing EP (2017b) Semantic pooling for complex event analysis in untrimmed videos. IEEE Trans Pattern Anal Mach Intell 39 (8):1617---1632

Digital Library

[21]

Chen W, Cao L, Chen X, Huang K (2014) A novel solution for multi-camera object tracking. In: 2014 IEEE International Conference on Image Processing (ICIP). IEEE, pp 2329---2333

[22]

Chen W, Cao L, Chen X, Huang K (2016)

[23]

Chen Y, Zhu X, Gong S et al (2018) Person re-identification by deep learning multi-scale representations

[24]

Colombo A, Orwell J, Velastin S et al (2008) Colour constancy techniques for re-recognition of pedestrians from multiple surveillance cameras. In: Workshop on multi-camera and multi-modal sensor fusion algorithms and applications-m2SFA2

[25]

Cong DNT, Khoudour L, Achard C, Meurie C, Lezoray O (2010) People re-identification by spectral classification of silhouettes. Signal Process 90(8):2362---2374

Digital Library

[26]

Cosar S, Çetin M (2013) A sparsity-driven approach to multi-camera tracking in visual sensor networks. In: AVSS. IEEE, pp 407---413

[27]

Dong Seon C, Marco C, Michele S, Loris B, Vittorio M (2011) Custom pictorial structures for re-identification. In: Proceedings of the British Machine Vision Conference. BMVA Press, pp 68.1---68.11.

[28]

Du W, Piater J (2006) Data fusion by belief propagation for multi-camera tracking. In: 2006 9th International Conference on Information Fusion, pp 1---8.

[29]

Du W, Piater JH (2007) Multi-camera people tracking by collaborative particle filters and principal axis-based integration. In: Yagi Y, Kang S B, Kweon I S, Zha H (eds) ACCV (1), Springer, lecture notes in computer science, vol 4843, pp 365---374

Digital Library

[30]

Eshel R, Moses Y (2010) Tracking in a dense crowd using multiple cameras. Int J Comput Vis 88(1):129---143

Digital Library

[31]

Evans M, Osborne CJ, Ferryman JM (2013) Multicamera object detection and tracking with object size estimation. In: AVSS. IEEE, pp 177---182

[32]

Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2010) Person re-identification by symmetry-driven accumulation of local features. In: CVPR. IEEE, pp 2360---2367

[33]

Felzenszwalb PF, Girshick RB, McAllester DA, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627---1645

Digital Library

[34]

Figueira D, Bazzani L, Minh HQ, Cristani M, Bernardino A, Murino V (2013) Semi-supervised multi-feature learning for person re-identification. In: AVSS. IEEE, pp 111---116

[35]

Fleuret F, Berclaz J, Lengagne R, Fua P (2008) Multicamera people tracking with a probabilistic occupancy map. IEEE Trans Pattern Ana Mach Intell 30 (2):267---282

Digital Library

[36]

Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: IEEE International workshop on performance evaluation of tracking and surveillance, Citeseer

[37]

Guan L, Franco JS, Pollefeys M (2010) Multi-view occlusion reasoning for probabilistic silhouette-based dynamic scene reconstruction. Int J Comput Vis 90 (3):283---303

Digital Library

[38]

Hamdoun O, Moutarde F, Stanciulescu B, Steux B (2008) Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences. In: ICDSC. IEEE, pp 1---6

[39]

Hirzer M, Beleznai C, Roth PM, Bischof H (2011) Person re-identification by descriptive and discriminative classification. In: Heyden A, Kahl F (eds) SCIA, vol 6688. Springer Lecture Notes in Computer Science, pp 91---102

Digital Library

[40]

Hofmann M, Wolf D, Rigoll G (2013) Hypergraphs for joint multi-view reconstruction and multi-object tracking. 2013 IEEE Conf Comput Vis Pattern Recogn 0:3650---3657.

Digital Library

[41]

Hong C, Yu J, Tao D, Wang M (2015a) Image-based three-dimensional human pose recovery by multiview locality-sensitive sparse retrieval. IEEE Trans Ind Electron 62(6):3742---3751

[42]

Hong C, Yu J, Wan J, Tao D, Wang M (2015b) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659---5670

Digital Library

[43]

Huang C, Wang SJ (2012) A bayesian hierarchical framework for multitarget labeling and correspondence with ghost suppression over multicamera surveillance system. IEEE T Autom Sci Eng 9(1):16---30

[44]

Javed O, Shafique K, Rasheed Z, Shah M (2008) Modeling inter-camera space-time and appearance relationships for tracking across non-overlapping views. Comput Vis Image Underst 109(2):146--- 162

Digital Library

[45]

Jiang X, Körner M, Haase D, Denzler J (2014) A graph-based map solution for multi-person tracking using multi-camera systems. In: Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP), pp 343---350

[46]

Jüngling K, Arens M (2011) View-invariant person re-identification with an implicit shape model. In: AVSS, IEEE Computer Society, pp 197---202

Digital Library

[47]

Kenk VS, Kovaă?iă? S, Kristan M, Hajdinjak M, Perš J et al (2015) Visual re-identification across large, distributed camera networks. Image Vis Comput 34:11---26

Digital Library

[48]

Khan SM, Shah M (2006) A multiview approach to tracking people in crowded scenes using a planar homography constraint. In: Leonardis A, Bischof H, Pinz A (eds) ECCV (4), Springer, Lecture notes in computer science, vol 3954, pp 133---146

Digital Library

[49]

Kim K, Davis LS (2006) Multi-camera tracking and segmentation of occluded people on ground plane using search- guided particle filtering. In: In ECCV, pp 98---109

Digital Library

[50]

Kroeger T, Dragon R, Van Gool L (2014) Multi-View Tracking of Multiple Targets with Dynamic Cameras. Springer, pp 653---665.

[51]

Köstinger M, Hirzer M, Wohlhart P, Roth PM, Bischof H (2012) Large scale metric learning from equivalence constraints. In: CVPR. IEEE, pp 2288---2295

Digital Library

[52]

Kuo CH, Huang C, Nevatia R (2010) Multi-target tracking by on-line learned discriminative appearance models. In: CVPR. IEEE, pp 685---692

[53]

Leal-Taixé L, Pons-Moll G, Rosenhahn B (2012) Branch-and-price global optimization for multi-view multi-target tracking. In: CVPR. IEEE, pp 1987---1994

Digital Library

[54]

Leibe B, Leonardis A, Schiele B (2008) Robust object detection with interleaved categorization and segmentation. Int J Comput Vis 77(1-3):259---289

Digital Library

[55]

Li W, Wu Y, Mukunoki M, Minoh M (2012) Common-near-neighbor analysis for person re-identification. In: 2012 19th IEEE International Conference on Image Processing (ICIP), pp 1621---1624.

[56]

Liem MC, Gavrila DM (2014) Joint multi-person detection and tracking from overlapping cameras. Comput Vis Image Underst 128:36---50

[57]

Lin Z, Davis LS (2008) Learning pairwise dissimilarity profiles for appearance recognition in visual surveillance. In: Bebis G, Boyle R D, Parvin B, Koracin D, Remagnino P, Porikli F M, Peters J, Klosowski J T, Arns L L, Chun Y K, Rhyne T M, Monroe L (eds) ISVC (1), Springer, lecture notes in computer science, vol 5358, pp 23---34

Digital Library

[58]

Loy CC, Liu C, Gong S (2013) Person re-identification by manifold ranking. In: ICIP, Citeseer, vol 1, pp 5

[59]

Martinel N, Micheloni C, Foresti GL (2015) Kernelized saliency-based person re-identification through multiple metric learning. IEEE Trans Image Process 24(12):5645---5658

Digital Library

[60]

Martinel N, Micheloni C, Foresti GL (2016) A pool of multiple person re-identification experts. Pattern Recogn Lett 71:23---30

Digital Library

[61]

Mazzon R, Cavallaro A (2013) Multi-camera tracking using a multi-goal social force model. Neurocomputing 100:41---50

Digital Library

[62]

Mazzon R, Tahir SF, Cavallaro A (2012) Person re-identification in crowd. Pattern Recogn Lett 33(14):1828---1837

Digital Library

[63]

Medeiros H, Park J, Kak A (2008) Distributed object tracking using a cluster-based kalman filter in wireless camera networks. IEEE J Sel Top Signal Process 2(4):448---463.

[64]

Nakajima C, Pontil M, Heisele B, Poggio T (2003) Full-body person recognition system. Pattern Recogn 36(9):1997---2006

[65]

Nie W, Liu A, Su Y, Luan H, Yang Z, Cao L, Ji R (2014) Single/cross-camera multiple-person tracking by graph matching. Neurocomputing 139:220---232

Digital Library

[66]

Patino Luis FJ (2014) Multicamera trajectory analysis for semantic behaviour characterisation. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, p 369---374

[67]

Prosser B, Zheng WS, Gong S, Xiang T (2010) Person re-identification by support vector ranking. In: Labrosse f, Zwiggelaar R, Liu Y, Tiddeman B (eds) BMVC, British Machine Vision Association, pp 1---11

[68]

Qian X, Fu Y, Jiang YG, Xiang T, Xue X (2017) Multi-scale deep learning architectures for person re-identification. arXiv:http://arXiv.org/abs/170905165

[69]

Quang MH, Bazzani L, Murino V (2013) A unifying framework for vector-valued manifold regularization and multi-view learning. In: Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp 100---108

Digital Library

[70]

Santos TT, Morimoto CH (2011) Multiple camera people detection and tracking using support integration. Pattern Recogn Lett 32 (1):47---55.

Digital Library

[71]

Schumann A, Monari E (2014) A soft-biometrics dataset for person tracking and re-identification. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp 193---198.

[72]

Schwartz WR, Davis LS (2009) Learning discriminative appearance-based models using partial least squares. In: SIBGRAPI. IEEE Computer Society, pp 322---329

Digital Library

[73]

Su C, Zhang S, Xing J, Gao W, Tian Q (2018) Multi-type attributes driven multi-camera person re-identification. Pattern Recogn 75:77---89

Digital Library

[74]

Taj M, Cavallaro A (2011) Distributed and decentralized multi-camera tracking. IEEE Signal Process Mag 28(3):46---58

[75]

Tuzel O, Porikli F, Meer P (2006) Region covariance: a fast descriptor for detection and classification. In: Leonardis A, Bischof H, Pinz A (eds) ECCV (2), Springer, lecture notes in computer science, vol 3952, pp 589---600

Digital Library

[76]

Vezzani R, Baltieri D, Cucchiara R (2013) People reidentification in surveillance and forensics: A survey. ACM Comput Surv 46(2):29:1---29:37.

Digital Library

[77]

Wang X, Doretto G, Sebastian T, Rittscher J, Tu PH (2007) Shape and appearance context modeling. In: ICCV. IEEE, pp 1---8

[78]

Wang Z, Hu R, Liang C, Yu Y, Jiang J, Ye M, Chen J, Leng Q (2016) Zero-shot person re-identification via cross-view consistency. IEEE Trans Multimed 18(2):260---272

Digital Library

[79]

Wang H, Yan Y, Hua J, Yang Y, Wang X, Li X, Deller JR, Zhang G, Bao H (2017a) Pedestrian recognition in multi-camera networks using multilevel important salient feature and multicategory incremental learning. Pattern Recogn 67:340---352

Digital Library

[80]

Wang J, Qian L, Azimi E, Kazanzides P (2017b) Prioritization and static error compensation for multi-camera collaborative tracking in augmented reality. In: 2017 IEEE Virtual Reality (VR). IEEE, pp 335---336

[81]

Wang J, Wang Z, Gao C, Sang N, Huang R (2017c) Deeplist: Learning deep features with adaptive listwise constraint for person reidentification. IEEE Trans Circuits Syst Video Techn 27(3):513---524

Digital Library

[82]

Wang Z, Hu R, Chen C, Yu Y, Jiang J, Liang C, Satoh S (2017d) Person reidentification via discrepancy matrix and matrix metric. IEEE transactions on cybernetics

[83]

Wen L, Lei Z, Chang MC, Qi H, Lyu S (2016) Multi-camera multi-target tracking with space-time-view hyper-graph. Int J Comput Vis 122(2):1---21

Digital Library

[84]

Wen L, Lei Z, Chang MC, Qi H, Lyu S (2017) Multi-camera multi-target tracking with space-time-view hyper-graph. Int J Comput Vis 122(2):313---333

Digital Library

[85]

Yao J, Odobez JM et al (2008) Multi-camera multi-person 3d space tracking with MCMC in surveillance scenarios. In: Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications-M2SFA2 2008, http://hal.inria.fr/inria-00326747/

[86]

Youlu W (2013) Distributed multi-object tracking with multi-camera systems composed of overlapping and non-overlapping cameras. PhD thesis, University of Nebraska-Lincoln

[87]

Zhang Z, Cheng J, et al. (2013) Multi-camera tracking helmet system. Journal of Image and Graphics 1(2):76---79

[88]

Zhao R, Ouyang W, Wang X (2013) Person re-identification by salience matching. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2528---2535

Digital Library

[89]

Zheng WS, Gong S, Xiang T (2013) Reidentification by relative distance comparison. IEEE Trans Pattern Anal Mach Intell 35(3):653---668

Digital Library

Cited By

Zhang QGong YChen DChan AHuang HWooldridge MDy JNatarajan S(2024)Multi-view people detection in large scenes via supervised view-wise contribution weightingProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i7.28553(7242-7250)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i7.28553
Córdoba-Tlaxcalteco MBenítez-Guerrero E(2023)Human Event Recognition in Smart Classrooms Using Computer Vision: A Systematic Literature ReviewProgramming and Computing Software10.1134/S036176882308006649:8(625-642)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1134/S0361768823080066
Zhang XXie XLai JZheng W(2023)Cross-Camera Trajectories Help Person Retrieval in a Camera NetworkIEEE Transactions on Image Processing10.1109/TIP.2023.329051532(3806-3820)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIP.2023.3290515
Show More Cited By

People tracking in multi-camera systems: a review
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks

Recommendations

A General Framework for Tracking Multiple People from a Moving Camera

In this paper, we present a general framework for tracking multiple, possibly interacting, people from a mobile vision platform. To determine all of the trajectories robustly and in a 3D coordinate system, we estimate both the camera's ego-motion and ...
Automatic scene calibration for detecting and tracking people using a single camera

Segmenting and tracking of people is an important aim in the video analysis with multitudinous applications. The scene calibration enables the system to process the input video in a different way depending on the camera position and the scene ...
Robust multi-camera 3D tracking from mono-camera 2d tracking using Bayesian Association

Visual tracking is essential for automatic scene understanding and surveillance of areas of interest. Monocular 2D tracking has been largely studied in the literature, but it usually provides inadequate or incomplete information for event ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 78, Issue 8

Apr 2019

1542 pages

ISSN:1380-7501

Issue’s Table of Contents

Copyright © Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 April 2019

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhang QGong YChen DChan AHuang HWooldridge MDy JNatarajan S(2024)Multi-view people detection in large scenes via supervised view-wise contribution weightingProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i7.28553(7242-7250)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i7.28553
Córdoba-Tlaxcalteco MBenítez-Guerrero E(2023)Human Event Recognition in Smart Classrooms Using Computer Vision: A Systematic Literature ReviewProgramming and Computing Software10.1134/S036176882308006649:8(625-642)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1134/S0361768823080066
Zhang XXie XLai JZheng W(2023)Cross-Camera Trajectories Help Person Retrieval in a Camera NetworkIEEE Transactions on Image Processing10.1109/TIP.2023.329051532(3806-3820)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIP.2023.3290515
Amosa TSebastian PIzhar LIbrahim OAyinla LBahashwan ABala ASamaila Y(2023)Multi-camera multi-object trackingNeurocomputing10.1016/j.neucom.2023.126558552:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.neucom.2023.126558
Bukhari MYasmin SNaz SMaqsood MRew JRho S(2023)Language and vision based person re-identification for surveillance systems using deep learning with LIP layersImage and Vision Computing10.1016/j.imavis.2023.104658132:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.imavis.2023.104658
Hasan MHanawa JGoto RSuzuki RFukuda HKuno YKobayashi Y(2022)LiDAR-based detection, tracking, and property estimationNeurocomputing10.1016/j.neucom.2022.07.087506:C(393-405)Online publication date: 28-Sep-2022
https://dl.acm.org/doi/10.1016/j.neucom.2022.07.087
Ji QDai CHou CLi X(2021)Real-time embedded object detection and tracking system in Zynq SoCJournal on Image and Video Processing10.1186/s13640-021-00561-72021:1Online publication date: 16-Jun-2021
https://dl.acm.org/doi/10.1186/s13640-021-00561-7

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents