构建校园场景下行人第一视角眼动数据集并提出导航眼动预测模型
EgoCampus: Egocentric Pedestrian Eye Gaze Model and Dataset
- 基于Meta Aria眼镜采集80+行人户外步行时的眼动数据
- 在6公里25条路径上构建首个面向室外导航的标注眼动数据集
- 新模型EgoCampusNet实现户外环境下的实时眼动预测，适合视觉注意力研究
为解决真实世界导航中人类视觉注意力预测难题，本文在大学校园户外环境中测量并建模了第一视角行人眼动。我们提出了EgoCampus数据集，涵盖6公里范围内的25条独立路径，收录超过80名行人的数据，形成多样化的带眼动标注视频序列。采集系统采用Meta的Project Aria眼镜，集成眼动追踪、前视RGB摄像头、惯性传感器与GPS，从人类视角提供多模态数据。与以往聚焦室内任务或缺少眼动信息的第一视角数据集不同，本工作重点研究行人行走时的视觉注意力。基于该数据集，我们开发了EgoCampusNet，一种用于预测行人在户外环境中移动时眼动的新方法。本研究不仅提供了一个研究真实世界注意力的新资源，也为未来导航场景下的眼动预测模型研究提供了支持。数据与代码将后续公开于https://github.com/ComputerVisionRutgers/EgoCampus。
原文摘要 · Abstract (English)
We address the challenge of predicting human visual attention during real-world navigation by measuring and modeling egocentric pedestrian eye gaze in an outdoor campus setting. We introduce the EgoCampus dataset, which spans 25 unique outdoor paths over 6 km across a university campus with recordings from more than 80 distinct human pedestrians, resulting in a diverse set of gaze-annotated videos. The system used for collection, Meta's Project Aria glasses, integrates eye tracking, front-facing RGB cameras, inertial sensors, and GPS to provide rich data from the human perspective. Unlike many prior egocentric datasets that focus on indoor tasks or exclude eye gaze information, our work emphasizes visual attention while subjects walk in outdoor campus paths. Using this data, we develop EgoCampusNet, a novel method to predict eye gaze of navigating pedestrians as they move through outdoor environments. Our contributions provide both a new resource for studying real-world attention and a resource for future work in gaze prediction models for navigation. Dataset and code will be made publicly available at a later date at https://github.com/ComputerVisionRutgers/EgoCampus .
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。