# Motion Segmentation Using Locally Affine Atom Voting

## TL;DR

LAAV introduces a novel motion segmentation method that uses feature-set affinities, simplifying complex scenarios and reducing computational costs while maintaining high accuracy, especially in noisy conditions.

## Contribution

LAAV is the first to use feature-set affinities for motion segmentation, improving efficiency and accuracy over pair-wise methods.

## Key findings

- LAAV achieves the most accurate motion segmentation results.
- LAAV performs comparably to other methods under measurement noise.
- LAAV reduces computational cost significantly.

## Abstract

We present a novel method for motion segmentation called LAAV (Locally Affine Atom Voting). Our model's main novelty is using sets of features to segment motion for all features in the scene. LAAV acts as a pre-processing pipeline stage for features in the image, followed by a fine-tuned version of the state-of-the-art Random Voting (RV) method. Unlike standard approaches, LAAV segments motion using feature-set affinities instead of pair-wise affinities between all features; therefore, it significantly simplifies complex scenarios and reduces the computational cost without a loss of accuracy. We describe how the challenges encountered by using previously suggested approaches are addressed using our model. We then compare our algorithm with several state-of-the-art methods. Experiments shows that our approach achieves the most accurate motion segmentation results and, in the presence of measurement noise, achieves comparable results to the other algorithms.