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.
@article{arxiv.2601.07154,
title = {Motion Focus Recognition in Fast-Moving Egocentric Video},
author = {Si-En Hong and James Tribble and Alexander Lake and Hao Wang and Chaoyi Zhou and Ashish Bastola and Siyu Huang and Eisa Chaudhary and Brian Canada and Ismahan Arslan-Ari and Abolfazl Razi},
journal= {arXiv preprint arXiv:2601.07154},
year = {2026}
}