Publication detail

Implicit Hand Gestures in Aeronautics Cockpit as a Cue for Crew State and Workload Inference

BEHÚŇ, K. HEROUT, A. PAVELKOVÁ, A.

Original Title

Implicit Hand Gestures in Aeronautics Cockpit as a Cue for Crew State and Workload Inference

English Title

Implicit Hand Gestures in Aeronautics Cockpit as a Cue for Crew State and Workload Inference

Type

conference paper

Language

en

Original Abstract

This paper aims at improving advanced aeronautic cockpit by raising its awareness of the crew's state and workload level. Our approach is based on visual analysis of pilot's upper body movements. We define the term of "implicit gestures" and further observe its subclasses. We collected a simulator dataset of practical implicit gestures, annotated semi-automatically a dataset for Human pose estimation training, and we offer these datasets for public use. Based on experiments on this data, we propose a method for recognition of implicit gestures - full interactions, touch-and-go interactions, and unfinished gestures. Our approach is purely visual (no depth data, which are hardly usable in the cockpit environment due to regulations). This method is based on human pose estimation by a hierarchical approach named Pose machine whose subsampled output is used for recognition of implicit gesture presence from sequences of frames by random forest. The experiments show that the classification works reliably and the method is able to recognize these implicit gestures in the cockpit.

English abstract

This paper aims at improving advanced aeronautic cockpit by raising its awareness of the crew's state and workload level. Our approach is based on visual analysis of pilot's upper body movements. We define the term of "implicit gestures" and further observe its subclasses. We collected a simulator dataset of practical implicit gestures, annotated semi-automatically a dataset for Human pose estimation training, and we offer these datasets for public use. Based on experiments on this data, we propose a method for recognition of implicit gestures - full interactions, touch-and-go interactions, and unfinished gestures. Our approach is purely visual (no depth data, which are hardly usable in the cockpit environment due to regulations). This method is based on human pose estimation by a hierarchical approach named Pose machine whose subsampled output is used for recognition of implicit gesture presence from sequences of frames by random forest. The experiments show that the classification works reliably and the method is able to recognize these implicit gestures in the cockpit.

Keywords

Implicit Gestures, Human Pose Estimation, Random Forest, Aeronautics Cockpit, Visual Recognition

RIV year

2015

Released

01.07.2015

Publisher

The Universidad de Las Palmas de Gran Canaria

Location

Las Palmas

ISBN

978-1-4673-6596-3

Book

Proceedings of ITSC 2015

Edition

NEUVEDEN

Edition number

NEUVEDEN

Pages from

632

Pages to

637

Pages count

6

Documents

BibTex


@inproceedings{BUT119871,
  author="Kamil {Behúň} and Adam {Herout} and Alena {Pavelková}",
  title="Implicit Hand Gestures in Aeronautics Cockpit as a Cue for Crew State and Workload Inference",
  annote="This paper aims at improving advanced aeronautic cockpit by raising its awareness
of the crew's state and workload level. Our approach is based on visual analysis
of pilot's upper body movements. We define the term of "implicit gestures" and
further observe its subclasses. We collected a simulator dataset of practical
implicit gestures, annotated semi-automatically a dataset for Human pose
estimation training, and we offer these datasets for public use. Based on
experiments on this data, we propose a method for recognition of implicit
gestures - full interactions, touch-and-go interactions, and unfinished gestures.
Our approach is purely visual (no depth data, which are hardly usable in the
cockpit environment due to regulations). This method is based on human pose
estimation by a hierarchical approach named Pose machine whose subsampled output
is used for recognition of implicit gesture presence from sequences of frames by
random forest. The experiments show that the classification works reliably and
the method is able to recognize these implicit gestures in the cockpit.",
  address="The Universidad de Las Palmas de Gran Canaria",
  booktitle="Proceedings of ITSC 2015",
  chapter="119871",
  doi="10.1109/ITSC.2015.109",
  edition="NEUVEDEN",
  howpublished="online",
  institution="The Universidad de Las Palmas de Gran Canaria",
  year="2015",
  month="july",
  pages="632--637",
  publisher="The Universidad de Las Palmas de Gran Canaria",
  type="conference paper"
}