Generative Model Based Video Shot Boundary Detection for Automated Surveillance

Abstract

Video shot boundary detection (SBD) or video cut detection is one of the fundamental processes of video-processing with respect to semantic understanding, contextual information accumulation, labeling, content-based information retrieval and many more applications, such as video surveillance and monitoring. In this work, the authors have proposed a generative-model based framework for detecting shot boundaries in between the frames of a video segment. To generate a model of shot-boundaries, the authors have applied the concepts of Support Vector Machine to estimate the distance between any two images, and then, have generated a Gaussian Mixture Model from the estimated distances. Next, a Bayesian Estimation process checks the presence of boundaries in between the images by exploiting the Gaussian Mixture-based boundary model. Further, the authors have used the principles of Compressive Sensing to reduce the overhead of boundary detection process without losing of important information.