A Unified Model Selection Technique for Spectral Clustering Based Motion
  Segmentation
Yuxiang Huang John Zelek
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.