EyeTrackUAV1

To the best of our knowledge, EyeTrackUAV1 is the first-ever published dataset of gaze deployment for the UAV imaging. It tackles the need for salience information to study the visual attention regarding this specific imaging. Indeed, content characteristics, further described below, may influence visualization behaviors in human observers.

Video Stimuli

The data collection was conducted on 19 sequences, which are 1280×720 and 30 fps.

They were extracted from the UAV123 database based on their high variability for specific content characteristics, namely:

• diversity of environment,
• distance and angle to the scene,
• size of the principal object, and
• presence of sky.

 

Gaze deployment information

Precise binocular gaze data were collected thanks to the EyeLink®, recording binocular eye positions at a rate of 1000 Hz, with a constructor accuracy of 0.25-0.50° of visual angle. The remote mode was used, enabling chinrest-free experiments, in view to let observers have a more natural content exploration.
14 observers participated, observing visual stimuli in free viewing conditions, in a controlled laboratory setup.

Overall, the dataset comprises eye-tracking information on 26599 frames, which represents 887 seconds of video.

Available information

From the dataset, you can download 

• • Gaze raw signals – transformed into the coordinate system of image sequences,
  • Saliency maps for every video frames – computed from raw signals without filtering,
  • Saccades and Fixation information – computed based on the implementation of EyeMMV‘s Dispersion-Threshold Identi cation (I-DT) algorithm.

All files can be freely downloaded from ftp://ftp.ivc.polytech.univ-nantes.fr/EyeTrackUAV.

Here is an example of raw signal-based saliency maps transparently overlapping the content boat8.

This work results from the collaboration of IRISA and LS2N within the framework of the ongoing research project ANR ASTRID DISSOCIE (Automated Detection of SaliencieS from Operators’ Point of View and Intelligent Compression of DronE videos) referenced as ANR-17-ASTR-0009.

If you use any of EyeTrackUAV1 data, please cite the following:

Krassanakis, V., Perreira Da Silva, M., & Ricordel, V. (2018). Monitoring Human Visual Behavior during the Observation of Unmanned Aerial Vehicles (UAVs) Videos. Drones, 2(4), 36