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

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 $(> 40\text{FPS})$, accuracy (MAE $=60\text{pixels})$, and robustness (SNR $=23\text{dB})$, confirming its potential to enhance the usability of vision-based assistive navigation tools in complex environments. The code is publicly available at https://arazi2.github.io/aisends.github.io/project/VisionGPT.