从第一视角视频中实时识别运动意图，助力体育与快速动作场景分析。
Motion Focus Recognition in Fast-Moving Egocentric Video
- 基于相机位姿基础模型，结合滑动批处理实现高效推理。
- 在自建数据集上实现实时性能，内存占用可控。
- 适合边缘部署，为快速运动场景提供新分析视角。
从视觉-语言-动作系统到机器人应用，现有第一视角数据集主要关注动作识别任务，而忽视了运动分析在体育及其他快速运动场景中的关键作用。为填补这一空白，我们提出一种实时运动焦点识别方法，可从任意第一视角视频中估计主体的移动意图。该方法利用相机位姿估计的基础模型，并引入系统级优化，实现高效且可扩展的推理。在自建的第一视角动作数据集上，通过滑动批处理推理策略，本方法实现了实时性能并保持了可管理的内存消耗。该工作使以运动为中心的分析适用于边缘部署，并为体育和快速运动活动研究提供了互补视角。
原文摘要 · Abstract (English)
From Vision-Language-Action (VLA) systems to robotics, existing egocentric datasets primarily focus on action recognition tasks, while largely overlooking the inherent role of motion analysis in sports and other fast-movement scenarios. To bridge this gap, we propose a real-time motion focus recognition method that estimates the subject's locomotion intention from any egocentric video. We leverage the foundation model for camera pose estimation and introduce system-level optimizations to enable efficient and scalable inference. Evaluated on a collected egocentric action dataset, our method achieves real-time performance with manageable memory consumption through a sliding batch inference strategy. This work makes motion-centric analysis practical for edge deployment and offers a complementary perspective to existing egocentric studies on sports and fast-movement activities.
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