  Gesture interaction is a natural way of communicating with a robot as an
alternative to speech. Gesture recognition methods leverage optical flow in
order to understand human motion. However, while accurate optical flow
estimation (i.e., traditional) methods are costly in terms of runtime, fast
estimation (i.e., deep learning) methods' accuracy can be improved. In this
paper, we present a pipeline for gesture-based human-robot interaction that
uses a novel optical flow estimation method in order to achieve an improved
speed-accuracy trade-off. Our optical flow estimation method introduces four
improvements to previous deep learning-based methods: strong feature
extractors, attention to contours, midway features, and a combination of these
three. This results in a better understanding of motion, and a finer
representation of silhouettes. In order to evaluate our pipeline, we generated
our own dataset, MIBURI, which contains gestures to command a house service
robot. In our experiments, we show how our method improves not only optical
flow estimation, but also gesture recognition, offering a speed-accuracy
trade-off more realistic for practical robot applications.
