# A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation

arXiv: https://arxiv.org/abs/2403.01606 · HTML: https://mlsanity.com/paper/2403.01606/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 3 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [Motion Segmentation by Exploiting Complementary Geometric Models](https://mlsanity.com/paper/1804.02142/) (2018)
- [Motion Segmentation from a Moving Monocular Camera](https://mlsanity.com/paper/2309.13772/) (2023)

## Built on by (most cited first, 2 of 2)

- [Zero-Shot Monocular Motion Segmentation in the Wild by Combining Deep Learning with Geometric Motion Model Fusion](https://mlsanity.com/paper/2405.01723/) (2024)
- [Dense Monocular Motion Segmentation Using Optical Flow and Pseudo Depth Map: A Zero-Shot Approach](https://mlsanity.com/paper/2406.18837/) (2024)

## Abstract (arXiv)

Motion segmentation is a fundamental problem in computer vision and is crucial in various applications such as robotics, autonomous driving and action recognition. Recently, spectral clustering based methods have shown impressive results on motion segmentation in dynamic environments. These methods perform spectral clustering on motion affinity matrices to cluster objects or point trajectories in the scene into different motion groups. However, existing methods often need the number of motions present in the scene to be known, which significantly reduces their practicality. In this paper, we propose a unified model selection technique to automatically infer the number of motion groups for spectral clustering based motion segmentation methods by combining different existing model selection techniques together. We evaluate our method on the KT3DMoSeg dataset and achieve competitve results comparing to the baseline where the number of clusters is given as ground truth information.