Fast automatic camera network calibration through human mesh recovery
Camera calibration is a necessary preliminary step in computer vision for the estimation of the position of objects in the 3D world. Despite the intrinsic camera parameters can be easily computed offline, extrinsic parameters need to be computed each time a camera changes its position, thus not allo...
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| Vydané v: | Journal of real-time image processing Ročník 17; číslo 6; s. 1757 - 1768 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.12.2020
Springer Nature B.V |
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| ISSN: | 1861-8200, 1861-8219 |
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| Abstract | Camera calibration is a necessary preliminary step in computer vision for the estimation of the position of objects in the 3D world. Despite the intrinsic camera parameters can be easily computed offline, extrinsic parameters need to be computed each time a camera changes its position, thus not allowing for fast and dynamic network re-configuration. In this paper we present an unsupervised and automatic framework for the estimation of the extrinsic parameters of a camera network, which leverages on optimised 3D human mesh recovery from a single image, and which does not require the use of additional markers. We show how it is possible to retrieve the real-world position of the cameras in the network together with the floor plane, exploiting regular RGB images and with a weak prior knowledge of the internal parameters. Our framework can also work with a single camera and in real-time, allowing the user to add, re-position, or remove cameras from the network in a dynamic fashion. |
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| AbstractList | Camera calibration is a necessary preliminary step in computer vision for the estimation of the position of objects in the 3D world. Despite the intrinsic camera parameters can be easily computed offline, extrinsic parameters need to be computed each time a camera changes its position, thus not allowing for fast and dynamic network re-configuration. In this paper we present an unsupervised and automatic framework for the estimation of the extrinsic parameters of a camera network, which leverages on optimised 3D human mesh recovery from a single image, and which does not require the use of additional markers. We show how it is possible to retrieve the real-world position of the cameras in the network together with the floor plane, exploiting regular RGB images and with a weak prior knowledge of the internal parameters. Our framework can also work with a single camera and in real-time, allowing the user to add, re-position, or remove cameras from the network in a dynamic fashion. |
| Author | Garau, Nicola Conci, Nicola De Natale, Francesco G. B. |
| Author_xml | – sequence: 1 givenname: Nicola surname: Garau fullname: Garau, Nicola email: nicola.garau@unitn.it organization: University of Trento – sequence: 2 givenname: Francesco G. B. surname: De Natale fullname: De Natale, Francesco G. B. organization: University of Trento – sequence: 3 givenname: Nicola surname: Conci fullname: Conci, Nicola organization: University of Trento |
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| Cites_doi | 10.1109/CVPR.2019.00576 10.1049/ip-vis:20010078 10.1109/ICCV.2019.00234 10.1007/978-3-642-24028-7_30 10.1109/TPAMI.2017.2699648 10.1088/1361-6501/aab4d6 10.1109/ICASSP.2016.7471849 10.1016/j.imavis.2017.12.006 10.1109/34.888718 10.1109/TCSVT.2017.2731792 10.1145/3204949.3204969 10.1109/CVPR.2017.143 10.1109/ACCESS.2019.2891224 10.1109/CVPR.2017.603 10.1109/CVPRW.2009.5206754 10.1145/3272127.3275014 10.1145/3349801.3349803 10.1109/CVPR.2018.00744 10.1145/2398356.2398381 10.1007/978-3-319-10605-2_3 10.1049/ip-vis:20010574 10.1007/978-3-319-10584-0_11 10.1016/j.patcog.2015.11.019 10.1109/MRA.2006.1638022 10.1109/CVPR.2016.511 10.1109/ICRA.2012.6224570 10.1109/ICCV.2015.381 10.1109/CVPR.2011.5995316 10.1109/CVPR.2018.00250 |
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Technol.201829606501310.1088/1361-6501/aab4d6 LoperMMBlackMJFleetDPajdlaTSchieleBTuytelaarsTOpendr: an approximate differentiable rendererComputer Vision–ECCV 20142014ChamSpringer15416910.1007/978-3-319-10584-0_11 SeoYHongKSTheory and practice on the self-calibration of a rotating and zooming camera from two viewsIEE Proceedings - Vision, Image and Signal Processing2001148316617210.1049/ip-vis:20010078 Peng, X.B., Kanazawa, A., Malik, J., Abbeel, P., Levine, S.: Sfv: Reinforcement learning of physical skills from videos. In: SIGGRAPH Asia 2018 Technical Papers. ACM, p. 178 (2018) Hidalgo, G., Raaj, Y., Idrees, H., Xiang, D., Joo, H., Simon, T., Sheikh, Y.: Single-network whole-body pose estimation (2019). arXiv preprint arXiv:1909.13423 Nistér, D., Naroditsky, O., Bergen, J.: Visual odometry. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004, vol. 1. 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Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR) 2011, pp. 1297–1304 (2011) JM Coughlan (1002_CR4) 2001 1002_CR31 Z Zhang (1002_CR34) 2000; 22 LL Presti (1002_CR23) 2016; 53 1002_CR11 1002_CR33 1002_CR10 1002_CR13 1002_CR12 F Vasconcelos (1002_CR32) 2018; 40 H Durrant-Whyte (1002_CR6) 2006; 13 Y Seo (1002_CR25) 2001; 148 1002_CR7 1002_CR8 1002_CR5 1002_CR9 1002_CR14 1002_CR16 1002_CR3 1002_CR19 1002_CR1 1002_CR2 G Zhang (1002_CR35) 2018; 29 1002_CR22 1002_CR21 H Kim (1002_CR15) 2001; 148 M Loper (1002_CR18) 2015; 34 F Zhao (1002_CR36) 2018; 70 Z Tang (1002_CR30) 2019; 7 S Miyata (1002_CR20) 2018; 28 MM Loper (1002_CR17) 2014 1002_CR26 J Shotton (1002_CR27) 2013; 56 1002_CR28 V Ramakrishna (1002_CR24) 2014 1002_CR29 |
| References_xml | – reference: LoperMMahmoodNRomeroJPons-MollGBlackMJSmpl: a skinned multi-person linear modelACM Trans. Gr. (TOG)2015346248 – reference: Nistér, D., Naroditsky, O., Bergen, J.: Visual odometry. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004, vol. 1. IEEE, pp. I–I (2004) – reference: Geiger, A., Moosmann, F., Car, Ö., Schuster, B.: Automaticcamera and range sensor calibration using a single shot. In: 2012IEEE International Conference on Robotics and Automation, pp. 3936–3943 (2012) – reference: Cao, Z., Simon, T., Wei, S., Sheikh, Y.: Realtime multi-person 2d pose estimation using part affinity fields. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1302–1310 (2017) – reference: TangZLinYLeeKHwangJChuangJEsther: Joint camera self-calibration and automatic radial distortion correction from tracking of walking humansIEEE Access20197107541076610.1109/ACCESS.2019.2891224 – reference: Kanazawa, A., Zhang, J.Y., Felsen, P., Malik, J.: Learning 3d human dynamics from video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5614–5623 (2019) – reference: Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7122–7131 (2018) – reference: KimHHongKSPractical self-calibration of pan-tilt camerasIEE Proc. Vis. Image Signal Process.2001148534935510.1049/ip-vis:20010574 – reference: Kolotouros, N., Pavlakos, G., Black, M.J., Daniilidis, K.: Learning to reconstruct 3d human pose and shape via model-fitting in the loop (2019). arXiv preprint arXiv:1909.12828 – reference: Tome, D., Russell, C., Agapito, L.: Lifting from the deep: Convolutional 3d pose estimation from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2500–2509 (2017) – reference: Simek, K.: Pinhole camera diagram, dissecting the camera matrix. http://ksimek.github.io/pinhole_camera_diagram/, 2013. Accessed 26 Apr 2019 – reference: Garau, N., Conci, N.: Unsupervised continuous camera network pose estimation through human mesh recovery. In: Proceedings of the 13th International Conference on Distributed Smart Cameras, ICDSC 2019, New York, NY, USA (2019) (Association for Computing Machinery) – reference: VasconcelosFBarretoJPBoyerEAutomatic camera calibration using multiple sets of pairwise correspondencesIEEE Trans. Pattern Anal. Mach. Intell.201840479180310.1109/TPAMI.2017.2699648 – reference: PrestiLLCasciaML3d skeleton-based human action classification: a surveyPattern Recogn.20165313014710.1016/j.patcog.2015.11.019 – reference: Hold-Geoffroy, Y., Sunkavalli, K., Eisenmann, J., Fisher, M., Gambaretto, E., Hadap, S., Lalonde, J.-F.: A perceptual measure for deep single image camera calibration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2354–2363 (2018) – reference: Joo, H., Liu, H., Tan, L., Gui, L., Nabbe, B., Matthews, I., Kanade, T., Nobuhara, S., Sheikh, Y.: Panoptic studio: A massively multiview system for social motion capture. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 3334–3342 (2015) – reference: LoperMMBlackMJFleetDPajdlaTSchieleBTuytelaarsTOpendr: an approximate differentiable rendererComputer Vision–ECCV 20142014ChamSpringer15416910.1007/978-3-319-10584-0_11 – reference: SeoYHongKSTheory and practice on the self-calibration of a rotating and zooming camera from two viewsIEE Proceedings - Vision, Image and Signal Processing2001148316617210.1049/ip-vis:20010078 – reference: Inomata, R., Terabayashi, K., Umeda, K., Godin, G.: Registration of 3d geometric model and color images using sift and range intensity images. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Wang, S., Kyungnam, K., Benes, B., Moreland, K., Borst, C., DiVerdi, S., Yi-Jen, Ming J. (Eds.), Advances in Visual Computing, Berlin, Heidelberg. Springer Berlin Heidelberg, pp. 325–336 (2011) – reference: Wei, S., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. 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