作者
Mohamed Selim, Ahmet Firintepe, Alain Pagani, Didier Stricker
发表日期
2020/2
研讨会论文
VISIGRAPP (4: VISAPP)
页码范围
599-606
简介
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∼ 1.1 M 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.
学术搜索中的文章
M Selim, A Firintepe, A Pagani, D Stricker - VISIGRAPP (4: VISAPP), 2020