Epipolar Contrained User Pushbutton Selection in Projected Interfaces
Abstract
An almost ubiquitous user interaction in most HCI applications is the task of selecting one of out of a given list of options. For example, in common desktop environments, the user moves the mouse pointer to the desired option and clicks it. The analog of this action in projector-camera HCI environments involves the user raising her finger to touch one of the different virtual buttons projected on a display surface. In this paper, we discuss some of the challenges involved in tracking and recognizing this task in an projected immersive environment and present a hierarchical vision based approach to detect intuitive gesture-based "mouse clicks" in a front-projected virtual interface. Given the difficulty of tracking user gestures directly in a projected environment, our approach first tracks shadows cast on the display by the user and exploits the multi-view geometry of the camera-projector pair to constrain a subsequent search for the users hand position in the scene. The method only requires a simple setup step in which the projector's epipole in the camera's frame is estimated. We demonstrate how this approach is capable of detecting a contact event as a user interacts with a virtual pushbutton display. Results demonstrate that camera-based monitoring of user gesture is feasible even under difficult conditions in which the user is illluminated by changing and saturated colors.
Cite
Text
Kale et al. "Epipolar Contrained User Pushbutton Selection in Projected Interfaces." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2004. doi:10.1109/CVPR.2004.332Markdown
[Kale et al. "Epipolar Contrained User Pushbutton Selection in Projected Interfaces." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2004.](https://mlanthology.org/cvpr/2004/kale2004cvpr-epipolar/) doi:10.1109/CVPR.2004.332BibTeX
@inproceedings{kale2004cvpr-epipolar,
title = {{Epipolar Contrained User Pushbutton Selection in Projected Interfaces}},
author = {Kale, Amit A. and Kwan, Kenneth and Jaynes, Christopher O.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2004},
pages = {156},
doi = {10.1109/CVPR.2004.332},
url = {https://mlanthology.org/cvpr/2004/kale2004cvpr-epipolar/}
}