UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition
Paper
Paper/Presentation Title | UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition |
---|---|
Presentation Type | Paper |
Authors | Perera, Asanka G., Law, Yee Wei and Chahl, Javaan |
Journal or Proceedings Title | Proceedings of European Conference on Computer Vision 2018 Workshops |
Journal Citation | 11130, pp. 117-128 |
Number of Pages | 12 |
Year | 2019 |
Publisher | Springer |
Place of Publication | Germany |
ISBN | 9783030110208 |
9783030110215 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-11012-3_9 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-030-11012-3_9 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-3-030-11012-3 |
Conference/Event | UAVision workshop, ECCV 2018 |
Event Details | UAVision workshop, ECCV 2018 |
Abstract | Current UAV-recorded datasets are mostly limited to action recognition and object tracking, whereas the gesture signals datasets were mostly recorded in indoor spaces. Currently, there is no outdoor recorded public video dataset for UAV commanding signals. Gesture signals can be effectively used with UAVs by leveraging the UAVs visual sensors and operational simplicity. To fill this gap and enable research in wider application areas, we present a UAV gesture signals dataset recorded in an outdoor setting. We selected 13 gestures suitable for basic UAV navigation and command from general aircraft handling and helicopter handling signals. We provide 119 high-definition video clips consisting of 37151 frames. The overall baseline gesture recognition performance computed using Pose-based Convolutional Neural Network (P-CNN) is 91.9%. All the frames are annotated with body joints and gesture classes in order to extend the dataset’s applicability to a wider research area including gesture recognition, action recognition, human pose recognition and situation awareness. |
Keywords | UAV; Gesture dataset; UAV control; Gesture recognition |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 4007. Control engineering, mechatronics and robotics |
Public Notes | The accessible file is the accepted version of the paper. Please refer to the URL for the published version. |
Series | Lecture Notes in Computer Science |
Byline Affiliations | University of South Australia |
Defence Science and Technology Group, Australia |
https://research.usq.edu.au/item/z77z0/uav-gesture-a-dataset-for-uav-control-and-gesture-recognition
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