A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation

arXiv:2403.01606v1 Announce Type: new
Abstract: 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 …

abstract action recognition applications arxiv autonomous autonomous driving clustering computer computer vision cs.ai cs.cv driving dynamic environments model selection recognition results robotics segmentation type unified model vision