A review of motion segmentation: Approaches and major challenges

Abstract

Motion segmentation has applications in, amongst others, robotics, traffic monitoring, sports analysis, inspection, video surveillance, compression, and video indexing. However, the performance of most methods is limited compared to human capabilities. Based on extensive literature the following challenges remain: occlusions, temporary stopping, missing data, and segmenting multiple objects. In this paper, several popular and state-of-the-art methods were reviewed, with the focus on the most important attributes. These methods were classified according to the main approach taken, namely Image Difference, Optical Flow, Wavelet, Statistical, Layers, Manifold Clustering, Template Matching, and Deep Learning. The investigated methods are compared and major research challenges are highlighted. Based on the review, improvements are identified as a basis for future research.