# Motor Focus: Fast Ego-Motion Prediction for Assistive Visual Navigation

arXiv: https://arxiv.org/abs/2404.17031 · HTML: https://mlsanity.com/paper/2404.17031/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021)
- [DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks](https://mlsanity.com/paper/1709.08429/) (2017)
- [Learning Optical Flow, Depth, and Scene Flow without Real-World Labels](https://mlsanity.com/paper/2203.15089/) (2022)
- [Self-Supervised 3D Keypoint Learning for Ego-motion Estimation](https://mlsanity.com/paper/1912.03426/) (2019)
- [Human Visual Attention Prediction Boosts Learning & Performance of Autonomous Driving Agents](https://mlsanity.com/paper/1909.05003/) (2019)
- [Real-Time Optical flow-based Video Stabilization for Unmanned Aerial Vehicles](https://mlsanity.com/paper/1701.03572/) (2017)
- [Fast Key Points Detection and Matching for Tree-Structured Images](https://mlsanity.com/paper/2211.03242/) (2022)
- [Robust Ego and Object 6-DoF Motion Estimation and Tracking](https://mlsanity.com/paper/2007.13993/) (2020)
- [3D Head-Position Prediction in First-Person View by Considering Head Pose for Human-Robot Eye Contact](https://mlsanity.com/paper/2103.06417/) (2021)
- [Energy Optimization for HVAC Systems in Multi-VAV Open Offices: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2306.13333/) (2023)

## Built on by (most cited first, 3 of 3)

- [RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection](https://mlsanity.com/paper/2407.12271/) (2024)
- [Fast 2DGS: Efficient Image Representation with Deep Gaussian Prior](https://mlsanity.com/paper/2512.12774/) (2025)
- [Motion Focus Recognition in Fast-Moving Egocentric Video](https://mlsanity.com/paper/2601.07154/) (2026)

## Abstract (arXiv)

Assistive visual navigation systems for visually impaired individuals have become increasingly popular thanks to the rise of mobile computing. Most of these devices work by translating visual information into voice commands. In complex scenarios where multiple objects are present, it is imperative to prioritize object detection and provide immediate notifications for key entities in specific directions. This brings the need for identifying the observer's motion direction (ego-motion) by merely processing visual information, which is the key contribution of this paper. Specifically, we introduce Motor Focus, a lightweight image-based framework that predicts the ego-motion - the humans (and humanoid machines) movement intentions based on their visual feeds, while filtering out camera motion without any camera calibration. To this end, we implement an optical flow-based pixel-wise temporal analysis method to compensate for the camera motion with a Gaussian aggregation to smooth out the movement prediction area. Subsequently, to evaluate the performance, we collect a dataset including 50 clips of pedestrian scenes in 5 different scenarios. We tested this framework with classical feature detectors such as SIFT and ORB to show the comparison. Our framework demonstrates its superiority in speed (> 40FPS), accuracy (MAE = 60pixels), and robustness (SNR = 23dB), confirming its potential to enhance the usability of vision-based assistive navigation tools in complex environments.