Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4969629 | Pattern Recognition | 2017 | 10 Pages |
Abstract
Detecting hand actions from ego-centric depth sequences is a practically challenging problem, owing mostly to the complex and dexterous nature of hand articulations as well as non-stationary camera motion. We address this problem via a Hough transform based approach coupled with a discriminatively learned error-correcting component to tackle the well known issue of incorrect votes from the Hough transform. In this framework, local parts vote collectively for the start & end positions of each action over time. We also construct an in-house annotated dataset. Our system is empirically evaluated on this real-life dataset as well as a synthetic dataset, where it is shown to deliver favorable results in real-time (around 112 frame-per-second). To facilitate reproduction, the new dataset and our implementation are also provided online.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Chi Xu, Lakshmi Narasimhan Govindarajan, Li Cheng,