# Motion Focus Recognition in Fast-Moving Egocentric Video

arXiv: https://arxiv.org/abs/2601.07154 · HTML: https://mlsanity.com/paper/2601.07154/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Builds on (most cited first, 4 of 4)

- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Depth Anything 3: Recovering the Visual Space from Any Views](https://mlsanity.com/paper/2511.10647/) (2025)
- [Agent-based Video Trimming](https://mlsanity.com/paper/2412.09513/) (2024)
- [Motor Focus: Fast Ego-Motion Prediction for Assistive Visual Navigation](https://mlsanity.com/paper/2404.17031/) (2024)

## Abstract (arXiv)

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.