首个真实城市环境下的第一视角行人轨迹数据集，支持多模态预测研究。
EgoTraj: Real-World Egocentric Human Trajectory Dataset for Multimodal Prediction
- 基于Meta Quest Pro采集75段第一视角导航序列，同步记录头部姿态与眼动数据。
- 首次实现长时序、自导向的多参与者城市路径数据采集，覆盖多样场景。
- 适合增强现实、助行系统与人机交互研究者使用，提供完整可视化工具。
从第一视角准确预测人类轨迹在类人机器人、可穿戴感知系统和辅助导航中具有重要意义。然而，由于真实世界环境下第一视角轨迹数据集稀缺，该领域进展受限。为此，我们提出EgoTraj，一个使用Meta Quest Pro（MQPro）在真实城市环境中采集的开放多模态数据集。EgoTraj包含75段由多位使用者在真实城市环境中进行的导航序列，每段记录均同步提供RGB视频、连续时间对齐的6自由度头部姿态、逐帧3D眼动向量及场景标注。据我们所知，EgoTraj区别于传统数据集之处在于其捕捉了长时序、自导向的城市路径导航，并涵盖广泛参与者多样性。为验证数据集潜力，我们对多个前沿第一视角轨迹预测方法进行了基准测试，并开展消融实验分析眼动、场景与运动线索的贡献。结果表明，EgoTraj适用于基于AR的感知、导航与辅助系统研究。EgoTraj数据集、代码及EgoViz可视化仪表盘已公开：https://github.com/yehiahmad/EgoTraj。
原文摘要 · Abstract (English)
Accurately forecasting human trajectories from an egocentric perspective plays a central role in applications such as humanoid robotics, wearable sensing systems, and assistive navigation. However, progress in this direction remains limited due to the scarcity of egocentric trajectory datasets collected in real-world environments. Addressing this need, we introduce EgoTraj, an egocentric multimodal open dataset recorded using Meta Quest Pro (MQPro). EgoTraj contains 75 sequences of human navigation collected from multiple MQPro wearers in real-world urban environments. Each recording provides synchronized RGB video along with ground-truth data, including continuous time-synchronized 6-degree-of-freedom head poses, per-frame 3D eye gaze vectors, scene annotations. To the best of our knowledge, EgoTraj differs from typical egocentric trajectory datasets by capturing long-horizon, self-directed navigation across diverse urban routes with broad participant diversity. To demonstrate the potential of the dataset, we benchmark several state-of-the-art methods for egocentric trajectory prediction and conduct ablation studies to analyze the contributions of gaze, scene, and motion cues. The results highlight the utility of EgoTraj for AR-based perception, navigation, and assistive systems. The EgoTraj dataset, code, and EgoViz Dashboard are publicly available at https://github.com/yehiahmad/EgoTraj.
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