In mobile eye-tracking research, the automatic annotation of fixation points is an important yet difficult task, especially in varied and dynamic environments such as outdoor urban landscapes. This complexity is increased by the constant movement and dynamic nature of both the observer and their environment in urban spaces. This paper presents a novel approach that integrates the capabilities of two foundation models, YOLOv8 and Mask2Former, as a pipeline to automatically annotate fixation points without requiring additional training or fine-tuning. Our pipeline leverages YOLO’s extensive training on the MS COCO dataset for object detection and Mask2Former’s training on the Cityscapes dataset for semantic segmentation. This integration not only streamlines the annotation process but also improves accuracy and consistency, ensuring reliable annotations, even in complex scenes with multiple objects side by side or at different depths. Validation through two experiments showcases its efficiency, achieving 89.05% accuracy in a controlled data collection and 81.50% accuracy in a real-world outdoor wayfinding scenario. With an average runtime per frame of 1.61 ± 0.35 s, our approach stands as a robust solution for automatic fixation annotation.

中文翻译：

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MYFix：眼球追踪视频的自动注视注释

在移动眼动追踪研究中，注视点的自动标注是一项重要但困难的任务，特别是在室外城市景观等变化多端的动态环境中。城市空间中观察者及其环境的不断运动和动态性​​质增加了这种复杂性。本文提出了一种新颖的方法，集成了两个基础模型 YOLOv8 和 Mask2Former 的功能，作为自动注释注视点的管道，无需额外的训练或微调。我们的管道利用 YOLO 在 MS COCO 数据集上的广泛训练来进行对象检测，并利用 Mask2Former 在 Cityscapes 数据集上进行的训练来进行语义分割。这种集成不仅简化了注释过程，而且提高了准确性和一致性，即使在多个并排或不同深度的对象的复杂场景中也能确保可靠的注释。通过两项实验的验证展示了其效率，在受控数据收集中实现了 89.05% 的准确度，在现实户外寻路场景中实现了 81.50% 的准确度。我们的方法每帧的平均运行时间为 1.61 ± 0.35 秒，是自动注视注释的强大解决方案。