Multimodal Hand Gesture Classification for the Human–Car Interaction

Abstract

The recent spread of depth sensors has enabled new methods to automatically estimate anthropometric measurements, in place of manual procedures or expensive 3D scanners. Generally, the use of depth data is limited by the lack of depth-based public datasets containing accurate anthropometric annotations. Therefore, in this paper we propose a new dataset, called Baracca, specifically designed for the automotive context, including in-car and outside views. The dataset is multimodal: it has been acquired with synchronized depth, infrared, thermal and RGB cameras in order to deal with the requirements imposed by the automotive context. In addition, we propose several baselines to test the challenges of the presented dataset and provide considerations for future work.

Recommended citation

@inproceedings{pini2020baracca,
  title={Baracca: a Multimodal Dataset for Anthropometric Measurements in Automotive},
  author={Pini, Stefano and D'Eusanio, Andrea and Borghi, Guido and Vezzani, Roberto and Cucchiara, Rita},
  booktitle={International Joint Conference on Biometrics (IJCB)},
  year={2020}
}