Hand Gestures for the Human-Car Interaction: the Briareo dataset

Natural User Interfaces can be an effective way to reduce driver's inattention during the driving activity. To this end, in this paper we propose a new dataset, called Briareo, specifically collected for the hand gesture recognition task in the automotive context. The dataset is acquired from an innovative point of view, exploiting different kinds of cameras, i.e. RGB, infrared stereo, and depth, that provide various types of images and 3D hand joints. Moreover, the dataset contains a significant amount of hand gesture samples, performed by several subjects, allowing the use of deep learning-based approaches. Finally, a framework for hand gesture segmentation and classification is presented, exploiting a method introduced to assess the quality of the proposed dataset.