Real-Time Storyboard Generation in Videos Using a Probability Distribution Based Threshold

Storyboard generation in real-time is a task which aims at producing a meaningful summary of a video in a duration which is comparable to its length. The main challenge in real-time videos is to extract a set of key-frames on the fly, without having access to the full content. A fast and accurate generation of a dynamic threshold is important to ensure that the summary produced is a succinct representation of the video and appropriate in terms of content coverage. In this work, an efficient method has been developed for producing a static summary using a probability distribution based threshold. An open framework has been used in the redundancy reduction phase which may incorporate any of the state-of-the-art similarity matching techniques so as to eliminate the superfluous key-frames. The efficacy of the proposed method is depicted through the high recall and precision values obtained from the experimental results on several publicly available videos of different genre and length. The method has been tested on computers having different system configurations, thereby proving the efficacy of the proposed technique in real-time.