Ahmet Firintepe
ABSTRACT
Augmented Reality Glasses usually implement an Inside-Out tracking. In case of a driving scenario or glasses with less computation capabilities, an Outside-In tracking approach is required. However, to the best of our knowledge, no public datasets exist that collects images of users wearing AR glasses. To address this problem, we present HMDPose, an infrared trinocular dataset of four different AR Head-mounted displays captured in a car. It contains sequences of 14 subjects captured by three different cameras running at 60 FPS each, adding up to more than 3,000,000 labeled images in total. We provide a ground truth 6DoF-pose, captured by a submillimeter accurate marker-based tracker. We make HMDPose publicly available for non-profit, academic use and non-commercial benchmarking on ags.cs.uni-kl.de/datasets/hmdpose/.
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- Guido Borghi, Marco Venturelli, Roberto Vezzani, and Rita Cucchiara. 2017. POSEidon: Face-From-Depth for Driver Pose Estimation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google Scholar
- Ahmet Firintepe, Selim Mohamed, Alain Pagani, and Didier Stricker. 2020. The More, the Merrier? A Study on In-Car IR-based Head Pose Estimation. In 2020 IEEE Intelligent Vehicles Symposium (IV). IEEE.Google ScholarDigital Library
- Anke Schwarz, Monica Haurilet, Manuel Martinez, and Rainer Stiefelhagen. 2017. DriveAHead-a large-scale driver head pose dataset. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 1–10.Google ScholarCross Ref
- Mohamed Selim, Ahmet Firintepe, Alain Pagani, and Didier Stricker. 2020. AutoPOSE: Large-Scale Automotive Driver Head Pose and Gaze Dataset with Deep Head Pose Baseline. In International Conference on Computer Vision Theory and Applications (VISAPP). SCITEPRESS Digital Library.Google Scholar
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