Scene Segmentation using multiple metrics
Video scene segmentation is often regarded as a primary step with regards to analysis of video data. The process of scene segmentation involves partitioning a video stream into scenes in which each scene is comprised of frames of similar content. This work may form a primary stage of larger system for automated quality control and image restoration that may be conducted in the BBC. Scene changes are can be categorised in two main classes, abrupt scene changes, in which a transition between different shots is made over a single frame and gradual scene changes, where a scene change is made across several frames. An example of a gradual scene change is a fade, where one scene is slowly blended to another scene, typically over tens of frames. This paper presents a novel approach of detecting abrupt scene changes. These types of changes make up a large majority of the scene transitions in most video footage and therefore are the most relevant to us. Many algorithms have already been published for the detection of scene changes; however, the detection rate is not sufficiently high enough for use in critical systems. This paper presents a literature review of existing algorithms for scene segmentation and then presents a method in which multiple frame based statistics and metrics may be combined to robustly identify abrupt scene changes. Results from the new algorithm are compared with results found in the literature and the detection rate is improved compared to previous art. This report is intended to provide an overview of the proposed algorithm, comparison with existing algorithms, with examples of some preliminary results.
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