Video Motion Segmentation Using New Adaptive Manifold Denoising Model

Abstract

Video motion segmentation techniques automatically segment and track objects and regions from videos or image sequences as a primary processing step for many computer vision applications. We propose a novel motion segmentation approach for both rigid and non-rigid objects using adaptive manifold denoising. We first introduce an adaptive kernel space in which two feature trajectories are mapped into the same point if they belong to the same rigid object. After that, we employ an embedded manifold denoising approach with the adaptive kernel to segment the motion of rigid and non-rigid objects. The major observation is that the non-rigid objects often lie on a smooth manifold with deviations which can be removed by manifold denoising. We also show that performing manifold denoising on the kernel space is equivalent to doing so on its range space, which theoretically justifies the embedded manifold denoising on the adaptive kernel space. Experimental results indicate that our algorithm, named Adaptive Manifold Denoising (AMD), is suitable for both rigid and non-rigid motion segmentation. Our algorithm works well in many cases where several state-of-the-art algorithms fail.