An overview of optical flow-based approaches for motion segmentation

The goal of motion segmentation is to segregate a visual scene into independently moving objects. It is an indispensable pre-processing step for various tasks in computer vision and has evolved as an active and flourishing research area in the last few decades. In the sequences captured using a monocular camera, motion segmentation is typically performed by analyzing apparent motion of pixels in the image plane, i.e. the optical flow. Optical flow is generally contemplated as an appropriate representation of image motion. Numerous techniques for reliable flow estimation and subsequent advancements in their framework have been proposed in the last couple of decades and are outlined briefly in this work. The paper attempts to give a summary of diverse optical flow-based approaches used for robust segmentation of static or dynamic scenes containing rigidly moving objects and discusses in brief the shortcomings associated with them.