Learning to Detect Touches on Cluttered Tables
Computer Vision and Pattern Recognition
2023-04-11 v1 Human-Computer Interaction
Abstract
We present a novel self-contained camera-projector tabletop system with a lamp form-factor that brings digital intelligence to our tables. We propose a real-time, on-device, learning-based touch detection algorithm that makes any tabletop interactive. The top-down configuration and learning-based algorithm makes our method robust to the presence of clutter, a main limitation of existing camera-projector tabletop systems. Our research prototype enables a set of experiences that combine hand interactions and objects present on the table. A video can be found at https://youtu.be/hElC_c25Fg8.
Keywords
Cite
@article{arxiv.2304.04687,
title = {Learning to Detect Touches on Cluttered Tables},
author = {Norberto Adrian Goussies and Kenji Hata and Shruthi Prabhakara and Abhishek Amit and Tony Aube and Carl Cepress and Diana Chang and Li-Te Cheng and Horia Stefan Ciurdar and Mike Cleron and Chelsey Fleming and Ashwin Ganti and Divyansh Garg and Niloofar Gheissari and Petra Luna Grutzik and David Hendon and Daniel Iglesia and Jin Kim and Stuart Kyle and Chris LaRosa and Roman Lewkow and Peter F McDermott and Chris Melancon and Paru Nackeeran and Neal Norwitz and Ali Rahimi and Brett Rampata and Carlos Sobrinho and George Sung and Natalie Zauhar and Palash Nandy},
journal= {arXiv preprint arXiv:2304.04687},
year = {2023}
}