Camera Tracking Systems and their Democratization

Metadata

Publisher: SMPTE
Doc Type: Journal Article
Content Type: Original Research
Abbreviated Title: SMPTE Motion Imaging Jour.
Volume: 135, No. 1, pp. 66–78
Abstract: Computer vision encompasses the analysis, processing, and interpretation of visual data. Tracking is a subset of this field, where systems recognize objects or salient features in a scene to determine their displacement across subsequent frames in a video stream. This facilitates automation, increases efficiency, and expands the functionality of these systems to applications in surveillance, medicine, and entertainment, among other fields. In recent years, Virtual Reality (VR) and Augmented Reality (AR) systems have gained popularity, prompting the development of camera tracking techniques. Camera tracking assesses the geometry and poses of a camera within a scene. Many tools are available to analyze and process camera tracking information, but most are proprietary, making information about them scarce; their availability to the general public also varies. To determine the democratization of the technology, three different tracking systems were compared. Two of these systems are standard tools used in the industry; the third system was a tracker built using OpenCV's open-source tools. A dataset of tracking values under different video parameters was gathered for all three trackers. By comparing and examining these results, it was determined that the tracking system was built using OpenCV and met industry standards. The impact of noise and lower resolution on the tracking system's performance was also assessed qualitatively by comparing tracking results in Unreal Engine. These results revealed that the democratization of tracking technology is limited by the equipment that the general public can access. This research aimed to understand better the workflow, optimization, and democratization of camera tracking systems.
Publication Date: 2026-01-01
DOI: 10.5594/JMI.2026/YGPF6760
ISSN: Print: 1545-0279 | Electronic: 2160-2492
Link: https://doi.org/10.5594/JMI.2026/YGPF6760
Author(s): Irene Munoz Lopez
Andrew Gilbert
Keyword(s): Camera Tracking, Open Source Tools, Unreal Engine, Optic Flow
Copyright: © 2026 SMPTE

Bibliographic Reference(s)

X. Feng, Y. Jiang, X. Yang, M. Du, and X. Li, “Computer Vision Algorithms and Hard-ware Implementations: A Survey,” Integration the VLSI Journal, 69 (11): 309–320, Nov. 2019, doi:10.1016/j.vlsi.2019.07.005. EXTERNAL
Y. Celik, M. Altun, and M. Gunes, “Color-Based Moving Object Tracking With an Active Camera Using Motion Information,” Proc. Int. Artit. Intell. Data Process. Symp. (IDAP), pp. 1–5, Sep. 2017. EXTERNAL
B. Chen, P. Li, C. Sun, D. Wang, G. Yang, and H. Lu, “Multi-Attention Module for Visual Tracking,” Pattern Recognit., 87: 80–93, Mar. 2019, doi:10.1016/j.patcog.2018.10.005. EXTERNAL
A. Pareek and N. Arora, “Re-Projected SURF Features-Based Mean-Shift Algorithm for Visual Tracking,” Procedia Comput. Sci., 167: 1553–1560, 2020, doi: 10.1016/j.procs.2020.03.366. EXTERNAL
H. Zhou, B. Ummenhofer, and T. Brox, “DeepTAM: Deep Tracking and Mapping With Convolutional Neural Networks,” Int. J Comput. Vis., 127(3): 411–430, Mar. 2019. EXTERNAL
J. Zhan, H. Zhao, P. Zheng, H. Wu, and L. Wang, “Salient Superpixel Visual Tracking With Graph Model and Iterative Segmentation,” Cogn. Comput., 13 (4): 848–863, Aug. 2021. EXTERNAL
A. Handa, R. Newcombe, A. Angeli, and D. Davidson, “Real-Time Camera Tracking: When Is High Frame-Rate Best?” Computer Vision - ECCV 2012, A. Fitzgibbon, Eds., Springer, Lecture Notes in Comput. Sci, vol. 7573, pp. 300–315, Oct. 2012. EXTERNAL
G. Younes, D. Asmar, E. Shammas, and J. Zelek, “Keyframe-Based Monocular SLAM: Design, Survey, and Future Directions,” Robot. Auton. Syst., (97): 1–22, Jan. 2017. EXTERNAL
Y. Genc, S. Riedel, F. Souvannavong, C. Akinlar, and N. Navab, “Marker-Less Tracking for AR: A Learning-Based Approach,” Proc. Int. Symp. Mixed Augmented Reality (ISMAR), pp. 1–10, Oct. 2002. EXTERNAL
K. Malvika and S. Malathi, “Insights Into the Impactful Usage of Virtual Reality for End Users,” Proc. Fourth Int. Conf. I-SMAC (IoT Soc. Mobile Analyt. Cloud), pp. 123–128, Oct. 2020. EXTERNAL
T. A. Syed, M. S. Siddiqui, H. B. Abdullah, S. Jan, A. Namoun, A. Alzahrani, A. Nadeem, and A. Alkhodre, “In-Depth Review of Augmented Reality: Tracking Technologies, Development Tools, AR Displays, Collaborative AR, and Security Concerns,” Sensors, Vol. 23, 2023. EXTERNAL
C. Okwuowulu and C. Michael, “A Technical Report on the Virtual Production of Marginalised Areas in the Pandemic Era,” Nigerian Theatre J, Vol. 21 (1):1–15, 2022. EXTERNAL
S. Kamat, Atmospheres of Change: Virtual Production, Ph.D. dissertation, Massa-chusetts Institute of Technology: Cambridge, MA, 2022. EXTERNAL
P. Nogueira, “Motion Capture Fundamentals,” Proc. Doctoral Symp. Informatics Eng., pp. 1–6. Sep. 2011. EXTERNAL
J. Meza, L. Romero, and A. Marrugo, “MarkerPose: Robust Real-Time Planar Target Tracking for Accurate Stereo Pose Estimation,” Proc. IEEE/CVF Cont. Computer Vision and Pattern Recognition (CVPR), pp.1–10, Jun. 2021. EXTERNAL
Adobe, “Tracking 3D Camera Movement,” Adobe After Effects Help, Nov. 21, 2023. [Online]. Available: https://helpx.adobe.com/after-effects/using/tracking-3d-camera-movement.html. EXTERNAL
Blender Foundation, “Track,” Blender Manual, online documentation, 2023. Accessed Nov. 24, 2023). [Online]. Available: https://docs.blender.org/manual/en/latest/movie_clip/tracking/ciiplediting/track.html. EXTERNAL
Blender Foundation, “Motion Tracking-Introduction,” Blender Manual, online documentation, 2023. [Online]. Available: https://docs.blender.org/manual/en/latest/movie_clip/tracking/introduction.html (accessed Nov. 24, 2023). EXTERNAL
N. Kadner, The Virtual Production Field Guide, Epic Games: Cary, NC, 2019. EXTERNAL
A. Chia, B. Keogh, D. Leorke, and B. Nicoll, “Platformisation in Game Development,” Internet Policy Rev., vol. 9 (4): pp. 1–22, Dec. 2020. EXTERNAL
OpenCV Team, “About OpenCV,” OpenCV. Accessed Feb. 20, 2024. [Online]. Available: https://opencv.org/about/. EXTERNAL
T. Ylanne, “3D Camera Tracking for Low-Budget Production,” M.S. thesis, Helsinki Metropolia Univ. Appl. Sci., Helsinki, Finland, 2011. EXTERNAL
D. Terzopoulos, “Perceptive Agents and Systems in Virtual Reality,” Proc. ACM Symp. Virtual Reality Software and Technology (VRST), pp. 1–10. Oct. 2003. EXTERNAL
V. Gaur, “Lucas-Kanade Optical Flow Machine Learning Implementations,” J. Student Res., 11 (3), Aug. 2022. EXTERNAL
OpenCV, “Optical Flow,” OpenCV Documentation, online documentation. Accessed Feb. 20, 2024. [Online]. Available: https://docs.opencv.org/4.x/d4/dee/tutorial_op-tical_flow.html. EXTERNAL
B. D. Lucas and T. Kanade, “An Iterative Image Registration Technique With an Application to Stereo Vision,” Proc. Int. Joint Conf. Artificial Intelligence (IJCAI), pp. 674–679, Aug. 1981. EXTERNAL
L. Mendes, A. Ricardo, A. Bernardino, and R. Ferreira, “A Comparative Study of Optical Flow Methods for Fluid Mechanics,” Exp. Fluids, Vol. 63, 2022. EXTERNAL
E. W. Weisstein, “Euler Angles,” Math World-A Wolfram Web Resource, 2009. Accessed Feb. 20, 2024. [Online]. Available: https://mathworld.wolfram.com/. EXTERNAL
A. Sivakumar, K. Shaw, and D. Pathak, “Robotic Telekinesis: Learning a Robotic Hand Imitator by Watching Humans on YouTube,” arXiv preprint arXiv: 2202.10448, 2022. EXTERNAL
M. Stoiber, M. Pfanne, K. Strobl, R. Triebel, and A. Albu-Schaffer, “A Sparse Gauss-ian Approach to Region-Based 6DoF Object Tracking,” Proc. Asian Conf. Computer Vision (ACCV), pp. 666–682, Nov.-Dec. 2020. EXTERNAL
Blender Foundation, “Tracking Marker,” Blender Manual, online documentation, 2023. Accessed Nov. 24, 2023. [Online]. Available: https://docs.blender.org/manu-al/en/latest/movie_clip/tracking/clip/marker.html. EXTERNAL
S. Condino, G. Turini, V. Mamone, P. Parchi, and V. Ferrari, “Hybrid Spine Simulator Prototype for X-Ray-Free Pedicle Screws Fixation Training,” Appl. Sci., Vol. 11, 2021. EXTERNAL
S. Jain, X. Zhang, Y. Zhou, G. Ananthanarayanan, J. Jiang, Y. Shu, P. Bahl, and J. Gonzalez, “Spatula: Efficient Cross-Camera Video Analytics on Large Camera Networks,” IEEE Trans. Mobile Comput., 2020. EXTERNAL
F. Svanstrom, C. Englund and F. Alonso-Fernandez, “Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors,” 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, pp. 7265–7272, 2021, doi: 10.1109/ICPR48806.2021.9413241. EXTERNAL
R. Salas-Moreno, R. Newcombe, H. Strasdat, P. Kelly, and A. J. Davison, “SLAM++: Simultaneous Localization and Mapping at the Level of Objects,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp.1352–1359, Jun. 2013. EXTERNAL
I. Ullah, X. Su, X. Zhang, and D. Choi, “Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter,” Wireless Commun. Mobile Comput., (1): 1–12, Jun. 2020. EXTERNAL

Source Data (JSON)

Full registry record with provenance metadata. Open directly: /api/doc/10.5594-JMI.2026-YGPF6760.json

Reference Tree

Explore all references and references to this document, as a navigable tree.

Open Reference Tree

Reference this Doc

Plain text (ISO 690 compliant)

Preview:

Irene Munoz Lopez and Andrew Gilbert; Camera Tracking Systems and their Democratization, MIJ 2026, Volume 135, Number 1 (pp. 66 to 78); SMPTE, 2026. Available at https://doi.org/10.5594/JMI.2026/YGPF6760

Snippet:

Irene Munoz Lopez and Andrew Gilbert; Camera Tracking Systems and their Democratization, MIJ 2026, Volume 135, Number 1 (pp. 66 to 78); SMPTE, 2026. Available at https://doi.org/10.5594/JMI.2026/YGPF6760

HTML (ISO 690 compliant)

Preview:

Snippet:

<span class="citation">Irene Munoz Lopez and Andrew Gilbert; <cite>Camera Tracking Systems and their Democratization</cite>, MIJ 2026, Volume 135, Number 1 (pp. 66 to 78); SMPTE, 2026. Available at <a href="https://doi.org/10.5594/JMI.2026/YGPF6760" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2026/YGPF6760</a></span>

SMPTE's HTML Pub

Preview:

Irene Munoz Lopez and Andrew Gilbert; Camera Tracking Systems and their Democratization, MIJ 2026, Volume 135, Number 1 (pp. 66 to 78); SMPTE, 2026
doi: 10.5594/JMI.2026/YGPF6760
url: https://doi.org/10.5594/JMI.2026/YGPF6760

Snippet:

<li>
Irene Munoz Lopez and Andrew Gilbert; <cite id="bib-10-5594-jmi-2026-ygpf6760">Camera Tracking Systems and their Democratization</cite>, MIJ 2026, Volume 135, Number 1 (pp. 66 to 78); SMPTE, 2026
<span class="doi">10.5594/JMI.2026/YGPF6760</span>
</li>