Rule of thumb: Deep derotation for improved fingertip detection

Aaron Wetzler, Ron Slossberg and Ron Kimmel

Abstract

We investigate a novel global orientation regression approach for articulated objects using a deep convolutional neural network. This is integrated with an in-plane image derotation scheme, DeROT, to tackle the problem of per-frame fingertip detection in depth images. The method reduces the complexity of learning in the space of articulated poses which is demonstrated by using two distinct state-of-the-art learning based hand pose estimation methods applied to fingertip detection. Significant classification improvements are shown over the baseline implementation. Our framework involves no tracking, kinematic constraints or explicit prior model of the articulated object in hand. To support our approach we also describe a new pipeline for high accuracy magnetic annotation and labeling of objects imaged by a depth camera.

Session

Poster 1

Files

Extended Abstract (PDF, 991K)
Paper (PDF, 2M)
Supplemental Materials (ZIP, 21M)

DOI

10.5244/C.29.33
https://dx.doi.org/10.5244/C.29.33

Citation

Bibtex

@inproceedings{BMVC2015_33,
	title={Rule of thumb: Deep derotation for improved fingertip detection},
	author={Aaron Wetzler and Ron Slossberg and Ron Kimmel},
	year={2015},
	month={September},
	pages={33.1-33.12},
	articleno={33},
	numpages={12},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.33},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.33}
}