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.

翻译：本研究通过模拟观察界面，探讨了视觉高亮在无人机监控任务中引导用户注意力的作用。实验结果表明，视觉高亮能够显著加速用户对相应区域的视觉注意。基于该观察，我们利用高亮中的时空信息开发了一种新的显著性模型——高亮信息显著性模型（HISM），用以推断高亮条件下视觉注意力的变化。研究结果验证了视觉高亮在增强用户注意力方面的有效性，并展示了将这些线索融入显著性预测模型的潜力。