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Autonomous UAV Cinematography: A Tutorial and a Formalized Shot-Type Taxonomy

Authors:

Ioannis Mademlis,

Nikos Nikolaidis,

Anastasios Tefas,

Alberto MessinaAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 52, Issue 5

Article No.: 105, Pages 1 - 33

https://doi.org/10.1145/3347713

Published: 13 September 2019 Publication History

Abstract

The emerging field of autonomous UAV cinematography is examined through a tutorial for non-experts, which also presents the required underlying technologies and connections with different UAV application domains. Current industry practices are formalized by presenting a UAV shot-type taxonomy composed of framing shot types, single-UAV camera motion types, and multiple-UAV camera motion types. Visually pleasing combinations of framing shot types and camera motion types are identified, while the presented camera motion types are modeled geometrically and graded into distinct energy consumption classes and required technology complexity levels for autonomous capture. Two specific strategies are prescribed, namely focal length compensation and multidrone compensation, for partially overcoming a number of issues arising in UAV live outdoor event coverage, deemed as the most complex UAV cinematography scenario. Finally, the shot types compatible with each compensation strategy are explicitly identified. Overall, this tutorial both familiarizes readers coming from different backgrounds with the topic in a structured manner and lays necessary groundwork for future advancements.

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References

[1]

A. Angeli, S. Doncieux, J.-A. Meyer, and D. Filliat. 2008. Real-time visual loop-closure detection. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). IEEE, 1842--1847.

[2]

I. Arev, H. S. Park, Y. Sheikh, J. K. Hodgins, and A. Shamir. 2014. Automatic editing of footage from multiple social cameras. ACM Transactions on Graphics (TOG) 33, 4 (2014), 81.

Digital Library

[3]

D. Avola, L. Cinque, G. L. Foresti, N. Martinel, D. Pannone, and C. Piciarelli. 2018. A UAV video dataset for mosaicking and change detection from low-altitude flights. IEEE Transactions on Systems, Man, and Cybernetics: Systems 99 (2018), 1--11.

[4]

B. Brown. 2016. Cinematography: Theory and Practice: Image Making for Cinematographers and Directors (3rd ed.). Focal Press.

[5]

P. Carr, M. Mistry, and I. Matthews. 2013. Hybrid robotic/virtual pan-tilt-zoom cameras for autonomous event recording. In Proceedings of the ACM International Conference on Multimedia. ACM.

Digital Library

[6]

E. Cheng. 2016. Aerial Photography and Videography Using Drones. Peachpit Press.

[7]

R. Couturier. 2013. Designing Scientific Applications on GPUs. CRC Press.

Digital Library

[8]

M. L. Cummings, A. Clare, and C. Hart. 2010. The role of human-automation consensus in multiple unmanned vehicle scheduling. Human Factors 52, 1 (2010), 17--27.

[9]

M. L. Cummings, J. P. How, A. Whitten, and O. Toupet. 2012. The impact of human-automation collaboration in decentralized multiple unmanned vehicle control. Proc. IEEE 100, 3 (2012), 660--671.

[10]

F. Daniyal and A. Cavallaro. 2011. Multi-camera scheduling for video production. In Proceedings of the IEEE Conference for Visual Media Production (CVMP).

Digital Library

[11]

H. Duan, Q. Luo, Y. Shi, and G. Ma. 2013. Hybrid particle swarm optimization and genetic algorithm for multi-UAV formation reconfiguration. IEEE Computational Intelligence Magazine 8, 3 (2013), 16--27.

Digital Library

[12]

C. Feichtenhofer, A. Pinz, and A. Zisserman. 2017. Detect to track and track to detect. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).

[13]

H. Fourati and D. E. C. Belkhiat. 2016. Multisensor Attitude Estimation: Fundamental Concepts and Applications. CRC Press LLC.

[14]

F. Fraundorfer and D. Scaramuzza. 2012. Visual odometry: Part II: Matching, robustness, optimization, and applications. IEEE Robotics 8 Automation Magazine 19, 2 (2012), 78--90.

[15]

FreeSkies. {n.d.}. FreeSkies CoPilot. http://freeskies.co/.

[16]

Q. Galvane, J. Fleureau, F.-L. Tariolle, and P. Guillotel. 2016. Automated cinematography with unmanned aerial vehicles. In Proceedings of the Workshop on Intelligent Camera Control, Cinematography and Editing (WICED).

Digital Library

[17]

V. Gandhi and R. Ronfard. 2015. A computational framework for vertical video editing. In Proceedings of the Workshop on Intelligent Camera Control, Cinematography and Editing (WICED).

Digital Library

[18]

X.-S. Gao, X.-R. Hou, J. Tang, and H.-F. Cheng. 2003. Complete solution classification for the perspective-three-point problem. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 8 (2003), 930--943.

Digital Library

[19]

Amber Garage. {n.d.}. Skywand. https://skywand.com/.

[20]

C. Gebhardt, B. Hepp, T. Nägeli, S. Stevšić, and O. Hilliges. 2016. Airways: Optimization-based planning of quadrotor trajectories according to high-level user goals. In Proceedings of the ACM Conference on Human Factors in Computing Systems.

Digital Library

[21]

I. Goodfellow, Y. Bengio, and A. Courville. 2016. Deep Learning. MIT Press.

Digital Library

[22]

M. S. Grewal, L. R. Weill, and A. P. Andrews. 2007. Global Positioning Systems, Inertial Navigation, and Integration. John Wiley 8 Sons.

Digital Library

[23]

G. Grisetti, R. Kummerle, C. Stachniss, and W. Burgard. 2010. A tutorial on graph-based SLAM. IEEE Intelligent Transportation Systems Magazine 2, 4 (2010), 31--43.

[24]

S. Hayat, E. Yanmaz, and R. Muzaffar. 2016. Survey on unmanned aerial vehicle networks for civil applications: A communications viewpoint. IEEE Communications Surveys 8 Tutorials 18, 4 (2016), 2624--2661.

Digital Library

[25]

A. Hocraffer and C. S. Nam. 2017. A meta-analysis of human-system interfaces in unmanned aerial vehicle (UAV) swarm management. Applied Ergonomics 58 (2017), 66--80.

[26]

B. K. P. Horn. 1987. Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America A 4, 4 (1987), 629--642.

[27]

X. Huang, R. Janaswamy, and A. Ganz. 2006. Scout: Outdoor localization using active RFID technology. In Proceedings of IEEE Conference on Broadband Communications, Networks and Systems (BROADNETS). 1--10.

[28]

C. Hung, Z. Xu, and S. Sukkarieh. 2014. Feature learning-based approach for weed classification using high resolution aerial images from a digital camera mounted on a UAV. Remote Sensing 6, 12 (2014), 12037--12054.

[29]

N. Joubert, D. B. Goldman, F. Berthouzoz, M. Roberts, J. A. Landay, and P. Hanrahan. 2016. Towards a drone cinematographer: Guiding quadrotor cameras using visual composition principles. arXiv preprint arXiv:1610.01691 (2016).

[30]

N. Joubert, M. Roberts, A. Truong, F. Berthouzoz, and P. Hanrahan. 2015. An interactive tool for designing quadrotor camera shots. ACM Transactions on Graphics (TOG) 34, 6 (2015), 238.

Digital Library

[31]

C. Ju and H. Son. 2018. Multiple UAV systems for agricultural applications: Control, implementation, and evaluation. Electronics 7, 9 (2018), 162.

[32]

E. Kakaletsis, M. Tzelepi, P. Kaplanoglou, C. Symeonidis, N. Nikolaidis, A. Tefas, and I. Pitas. 2019. Semantic map annotation through UAV video analysis using deep learning models in ROS. In Proceedings of the International Conference on Multimedia Modeling (MMM). Springer.

[33]

I. Karakostas, I. Mademlis, N. Nikolaidis, and I. Pitas. 2018. UAV cinematography constraints imposed by visual target tracking. In Proceedings of the IEEE International Conference on Image Processing (ICIP).

[34]

I. Karakostas, I. Mademlis, N. Nikolaidis, and I. Pitas. 2019. Shot type feasibility in autonomous UAV cinematography. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[35]

K. Kelchtermans and T. Tuytelaars. 2017. How hard is it to cross the room? Training (recurrent) neural networks to steer a UAV. arXiv preprint arXiv:1702.07600 (2017).

[36]

D. K. Kim and T. Chen. 2015. Deep neural network for real-time autonomous indoor navigation. arXiv preprint arXiv:1511.04668 (2015).

[37]

A. Kolling, P. Walker, N. Chakraborty, K. Sycara, and M. Lewis. 2016. Human interaction with robot swarms: A survey. IEEE Transactions on Human-Machine Systems 46, 1 (2016), 9--26.

[38]

W. Li, H. Fu, L. Yu, and A. Cracknell. 2016. Deep learning-based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sensing 9, 1 (2016), 22.

[39]

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg. 2016. SSD: Single shot multibox detector. In Proceedings of the European Conference on Computer Vision (ECCV). Springer, 21--37.

[40]

S. Lowry, N. Sünderhauf, P. Newman, J. J. Leonard, D. Cox, P. Corke, and M. J. Milford. 2016. Visual place recognition: A survey. IEEE Transactions on Robotics 32, 1 (2016), 1--19.

Digital Library

[41]

I. Mademlis, I. Mygdalis, C. Raptopoulou, N. Nikolaidis, N. Heise, T. Koch, J. Grunfeld, T. Wagner, A. Messina, F. Negro, S. Metta, and I. Pitas. 2017. Overview of drone cinematography for sports filming. In Proceedings of the European Conference on Visual Media Production (CVMP) (short).

[42]

I. Mademlis, V. Mygdalis, N. Nikolaidis, M. Montagnuolo, F. Negro, A. Messina, and I. Pitas. 2019. High-level multiple-UAV cinematography tools for covering outdoor events. IEEE Transactions on Broadcasting (2019).

[43]

I. Mademlis, V. Mygdalis, N. Nikolaidis, and I. Pitas. 2018. Challenges in autonomous UAV cinematography: An overview. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME).

[44]

I. Mademlis, N. Nikolaidis, A. Tefas, I. Pitas, T. Wagner, and A. Messina. 2019. Autonomous unmanned aerial vehicles filming in dynamic unstructured outdoor environments. IEEE Signal Processing Magazine 36, 1 (2019), 147--153.

[45]

L. Meier, P. Tanskanen, F. Fraundorfer, and M. Pollefeys. 2011. Pixhawk: A system for autonomous flight using onboard computer vision. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).

[46]

A. Messina, S. Metta, M. Montagnuolo, F. Negro, V. Mygdalis, I. Pitas, J. Capitán, A. Torres, S. Boyle, and D. Bull. 2018. The future of media production through multi-drones’ eyes. In Proceedings of the International Broadcasting Convention (IBC).

[47]

L. S. Monteiro, T. Moore, and C. Hill. 2005. What is the accuracy of DGPS? The Journal of Navigation 58, 2 (2005), 207--225.

[48]

G. Morgenthal and N. Hallermann. 2014. Quality assessment of unmanned aerial vehicle (UAV)-based visual inspection of structures. Advances in Structural Engineering 17, 3 (2014), 289--302.

[49]

E. Mouragnon, M. Lhuillier, M. Dhome, F. Dekeyser, and P. Sayd. 2009. Generic and real-time structure from motion using local bundle adjustment. Image and Vision Computing 27, 8 (2009), 1178--1193.

Digital Library

[50]

M. Mueller, V. Casser, N. Smith, and B. Ghanem. 2017. Teaching UAVs to race using UE4Sim. arXiv preprint arXiv:1708.05884 (2017).

[51]

M. Mueller, N. Smith, and B. Ghanem. 2016. A benchmark and simulator for UAV tracking. In Proceedings of European Conference on Computer Vision (ECCV). Springer.

[52]

R. Mur-Artal, J. M. M. Montiel, and J. D. Tardos. 2015. ORB-SLAM: A versatile and accurate monocular SLAM system. IEEE Transactions on Robotics 31, 5 (2015), 1147--1163.

Digital Library

[53]

R. Mur-Artal and J. D. Tardós. 2016. ORB-SLAM2: An open-source SLAM system for monocular, stereo and RGB-D cameras. arXiv preprint arXiv:1610.06475 (2016).

[54]

R. Mur-Artal and J. D. Tardós. 2017. Visual-inertial monocular SLAM with map reuse. IEEE Robotics and Automation Letters 2, 2 (2017), 796--803.

[55]

T. Nägeli, J. Alonso-Mora, A. Domahidi, D. Rus, and O. Hilliges. 2017. Real-time motion planning for aerial videography with dynamic obstacle avoidance and viewpoint optimization. IEEE Robotics and Automation Letters 2, 3 (2017), 1696--1703.

[56]

T. Nägeli, L. Meier, A. Domahidi, J. Alonso-Mora, and O. Hilliges. 2017. Real-time planning for automated multi-view drone cinematography. ACM Transactions on Graphics 36, 4 (2017), 132:1--132:10.

Digital Library

[57]

Tayyab Naseer, Jürgen Sturm, and Daniel Cremers. 2013. Followme: Person following and gesture recognition with a quadrocopter. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS).

[58]

A. Nemra and N. Aouf. 2010. Robust cooperative UAV visual SLAM. In IEEE International Conference on Cybernetic Intelligent Systems (CIS).

[59]

P. Nousi, E. Patsiouras, A. Tefas, and I. Pitas. 2018. Convolutional neural networks for visual information analysis with limited computing resources. In Proceedings of the IEEE International Conference on Image Processing (ICIP).

[60]

K.-K. Oh, M.-C. Park, and H.-S. Ahn. 2015. A survey of multi-agent formation control. Automatica 53 (2015), 424--440.

Digital Library

[61]

N. Passalis and A. Tefas. 2017. Bag-of-features pooling for deep convolutional neural networks. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).

[62]

N. Passalis and A. Tefas. 2017. Concept detection and face pose estimation using lightweight convolutional neural networks for steering drone video shooting. In Proceedings of EURASIP European Signal Processing Conference (EUSIPCO).

[63]

F. Patrona, I. Mademlis, A. Tefas, and I. Pitas. 2019. Computational UAV cinematography for intelligent shooting based on semantic visual analysis. In Proceedings of the IEEE International Conference on Image Processing (ICIP).

[64]

K. Pitstick, J. Hansen, M. Klein, E. Morris, and J. Vazquez-Trejo. 2018. Applying video summarization to aerial surveillance. In Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX. International Society for Optics and Photonics.

[65]

S. Ragi and E. K. P. Chong. 2013. UAV path planning in a dynamic environment via partially observable Markov decision process. IEEE Trans. Aerospace Electron. Systems 49, 4 (2013), 2397--2412.

[66]

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[67]

M. Roberts and P. Hanrahan. 2016. Generating dynamically feasible trajectories for quadrotor cameras. ACM Transactions on Graphics (TOG) 35, 4 (2016), 61.

Digital Library

[68]

F. Sadeghi and S. Levine. 2016. (CAD)2RL: Real single-image flight without a single real image. arXiv preprint arXiv:1611.04201 (2016).

[69]

A. Saeed, A. Abdelkader, M. Khan, A. Neishaboori, K. A. Harras, and A. Mohamed. 2017. On realistic target coverage by autonomous drones. arXiv preprint arXiv:1702.03456 (2017).

[70]

D. Scaramuzza and F. Fraundorfer. 2011. Visual odometry {tutorial}. IEEE Robotics 8 Automation Magazine 18, 4 (2011), 80--92.

[71]

P. Serra, R. Cunha, T. Hamel, D. Cabecinhas, and C. Silvestre. 2016. Landing of a quadrotor on a moving target using dynamic image-based visual servo control. IEEE Transactions on Robotics 32, 6 (2016), 1524--1535.

Digital Library

[72]

M. Shoaib, S. Bosch, O. D. Incel, H. Scholten, and P. J. M. Havinga. 2015. A survey of online activity recognition using mobile phones. Sensors 15, 1 (2015), 2059--2085.

[73]

C. Smith. 2016. The Photographer’s Guide to Drones. Rocky Nook.

[74]

Celine Teuliere, Laurent Eck, and Eric Marchand. 2011. Chasing a moving target from a flying UAV. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS).

[75]

A. Torres-González, J. Capitán, R. Cunha, A. Ollero, and I. Mademlis. 2017. A multidrone approach for autonomous cinematography planning. In Proceedings of Iberian Robotics Conference (ROBOT’).

[76]

D. Triantafyllidou, P. Nousi, and A. Tefas. 2017. Lightweight two-stream convolutional face detection. In Proceedings of EURASIP European Signal Processing Conference (EUSIPCO).

[77]

D. Triantafyllidou, P. Nousi, and A. Tefas. 2018. Fast deep convolutional face detection in the wild exploiting hard sample mining. Big Data Research 11 (2018), 65--76.

[78]

D. Triantafyllidou and A. Tefas. 2016. Face detection based on deep convolutional neural networks exploiting incremental facial part learning. In Proceedings of International Conference on Pattern Recognition (ICPR).

[79]

H. Trinh, J. Li, S. Miyazawa, J. Moreno, and S. Pankanti. 2012. Efficient UAV video event summarization. In Proceedings of the IEEE International Conference on Pattern Recognition (ICPR).

[80]

I. Tsingalis, A. Tefas, N. Nikolaidis, and I. Pitas. 2014. Shot type characterization in 2D and 3D video content. In Proceedings of IEEE International Workshop on Multimedia Signal Processing (MMSP).

[81]

A. Tsourdos, B. White, and M. Shanmugavel. 2010. Cooperative Path Planning of Unmanned Aerial Vehicles. Vol. 32. John Wiley 8 Sons.

[82]

M. Tzelepi and A. Tefas. 2017. Human crowd detection for drone flight safety using convolutional neural networks. In Proceedings of EURASIP European Signal Processing Conference (EUSIPCO).

[83]

M. Tzelepi and A. Tefas. 2019. Graph-embedded convolutional neural networks in human crowd detection for drone flight safety. IEEE Transactions on Emerging Topics in Computational Intelligence (2019).

[84]

G. Verghese. 1993. Perspective alignment back projection for monocular tracking of solid objects. In Proceedings of the British Machine Vision Conference (BMVC).

[85]

R. Viguier, C. C. Lin, H. AliAkbarpour, F. Bunyak, S. Pankanti, G. Seetharaman, and K. Palaniappan. 2015. Automatic video content summarization using geospatial mosaics of aerial imagery. In Proceedings of the IEEE International Symposium on Multimedia (ISM).

[86]

F. Xiao and Y. Jae Lee. 2016. Track and segment: An iterative unsupervised approach for video object proposals. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[87]

L. Yang, J. Qi, J. Xiao, and X. Yong. 2014. A literature review of UAV 3D path planning. In Proceedings of the IEEE World Congress on Intelligent Control and Automation (WCICA).

[88]

Y. Yuan, X. Liang, X. Wang, D.-Y. Yeung, and A. Gupta. 2017. Temporal dynamic graph LSTM for action-driven video object detection. In Proceedings of the IEEE International Conference on Computer Vision (ICCV).

[89]

O. Zachariadis, V. Mygdalis, I. Mademlis, N. Nikolaidis, and I. Pitas. 2017. 2D visual tracking for sports UAV cinematography applications. In Proceedings of the IEEE Global Conference on Signal and Information Processing (GlobalSIP) (2017).

[90]

J. Zhang and S. Singh. 2014. LOAM: LIDAR odometry and mapping in real-time. In Proceedings of Robotics: Science and Systems.

Cited By

Liu ZCheng ZLiu YJiang Q(2024)Identification Method of Unmanned Aerial Vehicle Graphical Control Strategy Based on Cloud ServerInternational Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142450001038:02Online publication date: 29-Feb-2024
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Montes-Grova MMygdalis VPérez-Grau FViguria APitas I(2024)Photoconsistent and Trajectory Guided Novel-View Synthesis Tool for UAV Cinematography Based on Autoregressive Transformers2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP)10.1109/MLSP58920.2024.10734750(1-6)Online publication date: 22-Sep-2024
https://doi.org/10.1109/MLSP58920.2024.10734750
Ye FHao WGao J(2024)Interference Cancelation for Downlink of Multidrone Auxiliary Communication System With SDMIEEE Internet of Things Journal10.1109/JIOT.2024.335382011:9(16627-16637)Online publication date: 1-May-2024
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Index Terms

Autonomous UAV Cinematography: A Tutorial and a Formalized Shot-Type Taxonomy
1. Applied computing
  1. Arts and humanities
    1. Media arts
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Vision for robotics
    2. Planning and scheduling
      1. Multi-agent planning
      2. Robotic planning

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 52, Issue 5

September 2020

791 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3362097

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering

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© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 13 September 2019

Accepted: 01 July 2019

Revised: 01 April 2019

Received: 01 January 2018

Published in CSUR Volume 52, Issue 5

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https://doi.org/10.1142/S0218001424500010
Montes-Grova MMygdalis VPérez-Grau FViguria APitas I(2024)Photoconsistent and Trajectory Guided Novel-View Synthesis Tool for UAV Cinematography Based on Autoregressive Transformers2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP)10.1109/MLSP58920.2024.10734750(1-6)Online publication date: 22-Sep-2024
https://doi.org/10.1109/MLSP58920.2024.10734750
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