A new trajectory clustering algorithm using temporal smoothness for motion segmentation

In this paper, a new trajectory clustering algorithm for motion segmentation is proposed. Our key contribution is to use temporal smoothness constraint to facilitate segmentation of incomplete trajectories, which leads to high robustness to missing data. We further show that most motions in foreground of a scene can be approximately represented by a set of translational motion models. Based on this assumption, a new clustering strategy is proposed to separate foreground objects from background. Finally, a series of experiments show that our approach is more effective and outperforms several state-of-the-art methods.