  Gestures form an important medium of communication between humans and
machines. An overwhelming majority of existing gesture recognition methods are
tailored to a scenario where humans and machines are located very close to each
other. This short-distance assumption does not hold true for several types of
interactions, for example gesture-based interactions with a floor cleaning
robot or with a drone. Methods made for short-distance recognition are unable
to perform well on long-distance recognition due to gestures occupying only a
small portion of the input data. Their performance is especially worse in
resource constrained settings where they are not able to effectively focus
their limited compute on the gesturing subject. We propose a novel, accurate
and efficient method for the recognition of gestures from longer distances. It
uses a dynamic neural network to select features from gesture-containing
spatial regions of the input sensor data for further processing. This helps the
network focus on features important for gesture recognition while discarding
background features early on, thus making it more compute efficient compared to
other techniques. We demonstrate the performance of our method on the LD-ConGR
long-distance dataset where it outperforms previous state-of-the-art methods on
recognition accuracy and compute efficiency.
