Inferring visual biases in uav videos from eye movements

Anne-Flore Perrin (1, 2) , Lu Zhang (3, 2) , Olivier Le Meur (1)

1 IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires
2 IETR - Institut d'Électronique et des Technologies du numéRique
3 INSA Rennes - Institut National des Sciences Appliquées - Rennes

Unmanned Aerial Vehicle (UAV) imagery is gaining a lot of momentum lately. Indeed, gathered information from a bird-point-of-view is particularly relevant for numerous applications, from agriculture to surveillance services. We herewith study visual saliency to verify whether there are tangible differences between this imagery and more conventional contents. We first describe typical and UAV contents based on their human saliency maps in a high-dimensional space, encompassing saliency map statistics, distribution characteristics, and other specifically designed features. Thanks to a large amount of eye tracking data collected on UAV, we stress the differences between typical and UAV videos, but more importantly within UAV sequences. We then designed a process to extract new visual attention biases in the UAV imagery, leading to the definition of a new dictionary of visual biases. We then conduct a benchmark on two different datasets, whose results confirm that the 20 defined biases are relevant as a low-complexity saliency prediction system.

Mots clés: Biases; Center bias; Eye tracking; Surveillance; Typical videos; Unmanned Aerial Vehicles (UAV); Visual attention; Visual salience

Domaines: Sciences de l'ingénieur [physics]

Anne-Flore Perrin, Lu Zhang, Olivier Le Meur. Inferring visual biases in uav videos from eye movements. Drones, 2020, 4 (3), pp.1-25. ⟨10.3390/drones4030031⟩. ⟨hal-03002080⟩