Shot Boundary Detection Framework For Video Editing Via Adaptive Thresholds And Gradual Curve Point

Abstract

Day to day huge volumes of extended videos entrained from documentaries, cinemas, athletics and surveillance cameras are evolving over video databases and in internet. Processing these videos manually is hard, costly and time-consuming. For extracting these long-duration videos an automatic procedure is desperately needed. As a vital factor the Shot boundary detection (SBD) is considered for lot of video analysis tasks, for example video editing, indexing, summarization and action recognition. In the analysis of video content SBD is considered to be one of the vital task. Based on this, we have presented an effective SBD approach. We have used the gradient and color information for abrupt transition detection. For Gradual transition detection the average edge information of the gradual curves in the sequence of frames are obtained. From the optimal edge detector an average edge frame is gained. The computational complexity is reduced by this approach by processing only the transition regions. The proposed approach when compared to the exiting work done have achieved improved results in terms of precision, recall and F1.