Journal
COMPUTER NETWORKS
Volume 205, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.comnet.2022.108781
Keywords
Dynamic hand gesture recognition; Hand gesture dataset; Human-computer interface; Touch-less screen; Computer vision
Ask authors/readers for more resources
In this article, a dataset consisting of 27 dynamic hand gesture types is proposed. The dataset was collected from 21 subjects at full HD resolution. The subjects were carefully instructed and monitored to ensure the accuracy of the gestures.
Computer vision systems are commonly used to design touch-less human-computer interfaces (HCI) based on dynamic hand gesture recognition (HGR) systems, which have a wide range of applications in several domains, such as, gaming, multimedia, automotive, home automation. However, automatic HGR is still a challenging task, mostly because of the diversity in how people perform the gestures. In addition, the number of publicly available hand gesture datasets is scarce, often the gestures are not acquired with sufficient image quality, and the gestures are not correctly performed. In this data article, we propose a dataset of 27 dynamic hand gesture types acquired at full HD resolution from 21 different subjects, which were carefully instructed before performing the gestures and monitored when performing the gesture; the subjects had to repeat the movement in case the performed hand gesture was not correct, i.e., the authors of this paper that were observing the gesture found that it did not correspond to the exact expected movement and/or the camera recorded a viewpoint did not allow for a plain visualizing of the gesture. Each subject performed 3 times the 27 hand gestures for a total of 1701 videos collected and corresponding 204,120 video frames.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available