Motion Focus Recognition in Fast-Moving Egocentric Video
This work addresses motion analysis for sports and fast-movement activities, offering a complementary perspective to existing egocentric studies, though it is incremental in nature.
The paper tackles the problem of motion focus recognition in fast-moving egocentric video by proposing a real-time method to estimate locomotion intention, achieving real-time performance with manageable memory consumption.
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. Our approach leverages the foundation model for camera pose estimation and introduces 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.