UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition

Paper

Perera, Asanka G., Law, Yee Wei and Chahl, Javaan. 2019. "UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition." UAVision workshop, ECCV 2018. Germany. Springer. https://doi.org/10.1007/978-3-030-11012-3_9
Paper/Presentation Title

UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition

Presentation TypePaper
AuthorsPerera, Asanka G., Law, Yee Wei and Chahl, Javaan
Journal or Proceedings TitleProceedings of European Conference on Computer Vision 2018 Workshops
Journal Citation11130, pp. 117-128
Number of Pages12
Year2019
PublisherSpringer
Place of PublicationGermany
ISBN9783030110208
9783030110215
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-11012-3_9
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/978-3-030-11012-3_9
Web Address (URL) of Conference Proceedingshttps://link.springer.com/book/10.1007/978-3-030-11012-3
Conference/EventUAVision 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.

KeywordsUAV; Gesture dataset; UAV control; Gesture recognition
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204007. 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.

SeriesLecture Notes in Computer Science
Byline AffiliationsUniversity of South Australia
Defence Science and Technology Group, Australia
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