Abstract
The fast and tremendous evolution of the unmanned aerial vehicle (UAV) imagery gives place to the multiplication of applications in various fields such as military and civilian surveillance, delivery services, and wildlife monitoring. Combining UAV imagery with study of dynamic salience further extends the number of future applications. Indeed, considerations of visual attention open the door to new avenues in a number of scientific fields such as compression, retargeting, and decision-making tools. To conduct saliency studies, we identified the need for new large-scale eye-tracking datasets for visual salience in UAV content. Therefore, we address this need by introducing the dataset EyeTrackUAV2. It consists of the collection of precise binocular gaze information (1000 Hz) over 43 videos (RGB, 30 fps, 1280 × 720 or 720 × 480). Thirty participants observed stimuli under both free viewing and task conditions. Fixations and saccades were then computed with the dispersion-threshold identification (I-DT) algorithm, while gaze density maps were calculated by filtering eye positions with a Gaussian kernel. An analysis of collected gaze positions provides recommendations for visual salience ground-truth generation. It also sheds light upon variations of saliency biases in UAV videos when opposed to conventional content, especially regarding the center bias.
Highlights
For a couple of decades we have witnessed the fast advances and growing use of unmanned aerial vehicle (UAV) for multiple critical applications
We showed that saliency models that heavily rely on the center bias were less efficient on Unmanned Aerial Vehicle (UAV) videos than on conventional video sequences
UAV imaging modifies the perceptual clues of typical scenes due to its bird point of view, the presence of camera movements and the high distance and angle to the scene
Summary
For a couple of decades we have witnessed the fast advances and growing use of UAV for multiple critical applications. UAV refers here to unmanned aerial vehicles, autonomous or monitored from remote sites. This imagery enables a broad range of uses, from making vacation movies to drone races for mainstream civilian applications. Most applications are military, from aerial surveillance [4], drone-based warfare [5] to moving targets tracking [6], object, person, and anomaly detection [7,8,9]. The bird point of view modifies the perspective, size and features of objects [11]. It introduces a loss of pictorial depth cues [12] such as horizontal line [13]. UAV high autonomy in conjunction with large-field of view camera permits
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