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.

该论文提出了一种统一的模型选择技术，通过结合不同的现有模型选择技术，自动推断用于基于谱聚类的运动分割方法的运动组数，并在 KT3DMoSeg 数据集上评估了该方法在给定簇数为基准信息时与基准方法相比所取得的有竞争力的结果。