How well current saliency prediction models perform on UAVs videos?

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

1 IRISA-D6 - MEDIA ET INTERACTIONS
2 PERCEPT - Computational Visual Perception and Applications
3 IETR - Institut d'Électronique et des Technologies du numéRique

It is exciting to witness the fast development of Unmanned Aerial Vehicle (UAV) imaging which opens the door to many new applications. In view of developing rich and efficient services, we wondered which strategy should be adopted to predict salience in UAV videos. To that end, we introduce here a benchmark of off-the-shelf state-of-the-art models for saliency prediction. This benchmark studies comprehensively two challenging aspects related to salience, namely the peculiar characteristics of UAV contents and the temporal dimension of videos. This paper enables to identify the strengths and weaknesses of current static, dynamic, supervised and unsupervised models for drone videos. Eventually, we highlight several strategies for the development of visual attention in UAV videos.

Domaines: Traitement du signal et de l'image [eess.SP]

Anne-Flore Perrin, Lu Zhang, Olivier Le Meur. How well current saliency prediction models perform on UAVs videos?. CAIP (International Conference on Computer Analysis of Images and Patterns), Sep 2019, Salermo, Italy. ⟨10.1007/978-3-030-29888-3_25⟩. ⟨hal-02265047⟩