Enhancing Saliency Prediction in Monitoring Tasks: The Role of Visual Highlights
This work addresses attention guidance in drone monitoring, but it is incremental as it builds on existing saliency prediction methods by incorporating visual highlights.
The study tackled the problem of guiding user attention in drone monitoring tasks by using visual highlights, finding that highlights significantly expedite visual attention on targeted areas and that a new highlight-informed saliency model (HISM) effectively predicts attention changes.
This study examines the role of visual highlights in guiding user attention in drone monitoring tasks, employing a simulated interface for observation. The experiment results show that such highlights can significantly expedite the visual attention on the corresponding area. Based on this observation, we leverage both the temporal and spatial information in the highlight to develop a new saliency model: the highlight-informed saliency model (HISM), to infer the visual attention change in the highlight condition. Our findings show the effectiveness of visual highlights in enhancing user attention and demonstrate the potential of incorporating these cues into saliency prediction models.