AutoPOSE: Large-Scale Automotive Driver Head Pose and Gaze Dataset with Deep Head Pose Baseline

AutoPOSE: Large-Scale Automotive Driver Head Pose and Gaze Dataset with Deep Head Pose Baseline
Mohamed Selim, Ahmet Firintepe, Alain Pagani, Didier Stricker
International Conference on Computer Vision Theory and Applications (VISAPP). International Conference on Computer Vision Theory and Applications (VISAPP-2020) 15th February 27-29 Valletta Malta SCITEPRESS Digital Library 2020 .

Abstract:

In computer vision research, public datasets are crucial to objectively assess new algorithms. By the wide use of deep learning methods to solve computer vision problems, large-scale datasets are indispensable for proper network training. Various driver-centered analysis depend on accurate head pose and gaze estimation. In this paper, we present a new large-scale dataset, AutoPOSE. The dataset provides ∼ 1.1M IR images taken from the dashboard view, and ∼ 315K from Kinect v2 (RGB, IR, Depth) taken from center mirror view. AutoPOSE’s ground truth -head orientation and position-was acquired with a sub-millimeter accurate motion capturing system. Moreover, we present a head orientation estimation baseline with a state-of-the-art method on our AutoPOSE dataset. We provide the dataset as a downloadable package from a public website.