UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition
This dataset addresses a gap for researchers in UAV control and gesture recognition by providing outdoor-recorded data, though it is incremental as it builds on existing gesture datasets.
The authors tackled the lack of an outdoor public video dataset for UAV gesture recognition by creating UAV-GESTURE, a dataset with 119 high-definition video clips and 37,151 frames, achieving a baseline gesture recognition performance of 91.9% using a Pose-based Convolutional Neural Network.
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.