Improved Optical Flow for Gesture-based Human-robot Interaction
This work addresses the need for more efficient and accurate gesture recognition in practical robot applications, though it appears incremental as it builds on existing deep learning-based optical flow methods.
The paper tackled the problem of improving optical flow estimation for gesture-based human-robot interaction by developing a novel method that enhances the speed-accuracy trade-off, resulting in better gesture recognition performance on a custom dataset.
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.