  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 of 300 long videos, containing 3,177 single-action
subsequences over 16 action classes collected from 26 individuals. Our system
is empirically evaluated on this real-life dataset for both the action
recognition and detection tasks, and is shown to produce satisfactory results.
To facilitate reproduction, the new dataset and our implementation are also
provided online.
