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Applications, databases and open computer vision research from drone videos and images: a survey

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Abstract

Analyzing videos and images captured by unmanned aerial vehicles or aerial drones is an emerging application attracting significant attention from researchers in various areas of computer vision. Currently, the major challenge is the development of autonomous operations to complete missions and replace human operators. In this paper, based on the type of analyzing videos and images captured by drones in computer vision, we have reviewed these applications by categorizing them into three groups. The first group is related to remote sensing with challenges such as camera calibration, image matching, and aerial triangulation. The second group is related to drone-autonomous navigation, in which computer vision methods are designed to explore challenges such as flight control, visual localization and mapping, and target tracking and obstacle detection. The third group is dedicated to using images and videos captured by drones in various applications, such as surveillance, agriculture and forestry, animal detection, disaster detection, and face recognition. Since most of the computer vision methods related to the three categories have been designed for real-world conditions, providing real conditions based on drones is impossible. We aim to explore papers that provide a database for these purposes. In the first two groups, some survey papers presented are current. However, the surveys have not been aimed at exploring any databases. This paper presents a complete review of databases in the first two groups and works that used the databases to apply their methods. Vision-based intelligent applications and their databases are explored in the third group, and we discuss open problems and avenues for future research.

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Notes

  1. Workshop in conjunction with International Conference on Computer Vision: https://sites.google.com/site/uavision2017/.

  2. Workshop in conjunction with European Conference on computer vision: https://sites.google.com/site/uavision2018/.

  3. Workshop in conjunction with Conference on Computer Vision and Pattern Recognition: https://sites.google.com/site/uavision2019/.

  4. https://www.pix4d.com/.

  5. http://eros.usgs.gov/aerial-photography.

  6. http://sipi.usc.edu/database/database.php?volume=aerials.

  7. http://www.gpsinformation.org/dale/nmea.htm.

  8. https://pixhawk.org.

  9. https://www.parrot.com/us/drones/parrot-bebop-2.

  10. https://www.bitcraze.io/crazyflie-2/.

  11. https://link.springer.com/article/10.1007%2Fs10846-018-0954-x.

  12. http://viper-toolkit.sourceforge.net/.

  13. Available at http://www.youtube.com/.

  14. Available at https://www.sensefly.com/drones/example-datasets.html.

  15. Available at https://ivul.kaust.edu.sa/Pages/Dataset-UAV123.aspx.

  16. https://www.sensefly.com/drones/ebee.html.

  17. CanonDIGITALIXUS120IS_5.0_3000x4000.

  18. Available at https://www.crcv.ucf.edu/data/UCF_Aerial_Action.php.

  19. https://www.dji.com/.

  20. Available at https://www.sr-research.com/eyelink-1000-plus/.

  21. https://www.flir.com.

  22. DJI—The World Leader in Camera Drones/Quadcopters for Aerial Photography.

  23. http://kuzikus-namibia.de/xe_index.html.

  24. https://sites.nicholas.duke.edu/uas/.

  25. https://sites.nicholas.duke.edu/uas/.

  26. http://droneadventures.org/.

  27. http://cooperation.epfl.ch/.

  28. Available at http://www.reuters.com/news/picture/ruins-of-haitis-national-palace?articleId=USRTR370GT.

  29. Available at http://environmentalheadlines.com/ct/2011/09/01/new-england-feels-hurricane-irene%E2%80%99s-impacts/hurricane-irene-damage-ct-nat-guard-east-haven.

  30. Available at http://www.defense.gov/Media/Photo-Gallery?igphoto=2001185999.

  31. Available at http://www.chicagotribune.com/news/nationworld/83269837-132.html.

  32. http://abc7chicago.com/news/illinois-tornado-victims-how-to-help-/648502/.

  33. Nazr means “sight” in Arabic.

  34. http://gettyimages.com.

  35. Available at http://www.dronestagr.am/.

  36. https://openaerialmap.org/.

  37. https://github.com/openimagerynetwork.

  38. http://coastalresilience.org/project-areas/california/el-nino-california/.

  39. http://droneadventures.org/.

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Acknowledgements

This publication was made possible by NPRP Grant # NPRP8-140-2-065 from Qatar National Research Fund (a member of Qatar Foundation). The statement made herein are solely the responsibility of the authors.

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    Younes Akbari, Noor Almaadeed, Somaya Al-maadeed & Omar Elharrouss

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Akbari, Y., Almaadeed, N., Al-maadeed, S. et al. Applications, databases and open computer vision research from drone videos and images: a survey. Artif Intell Rev 54, 3887–3938 (2021). https://doi.org/10.1007/s10462-020-09943-1

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