In computer vision, rigid motion segmentation is the process of separating regions, features, or trajectories from a video sequence into coherent subsets of space and time. These subsets correspond to independent rigidly moving objects in the scene. The goal of this segmentation is to differentiate and extract the meaningful rigid motion from the background and analyze it. Image segmentation techniques labels the pixels to be a part of pixels with certain characteristics at a particular time. Here, the pixels are segmented depending on its relative movement over a period of time i.e. the time of the video sequence.
There are a number of methods that have been proposed to do so. There is no consistent way to classify motion segmentation due to its large variation in literature. Depending on the segmentation criterion used in the algorithm it can be broadly classified into the following categories: image difference, statistical methods, wavelets, layering, optical flow and factorization. Moreover depending on the number of views required the algorithms can be two or multi view-based. Rigid motion segmentation has found an increase in its application over the recent past with rise in surveillance and video editing. These algorithms are discussed further.
