Speeded-Up Video Summarization Based on Local Features

Abstract

Digital video has become a very popular media in several contexts, with an ever expanding horizon of applications and uses. Thus, the amount of available video data is growing almost limitless. For this reason, video summarization continues to attract the attention of a wide spectrum of research efforts. In this work we present a novel video summarization technique based on tracking local features among consecutive frames. Our approach operates on the uncompressed domain, and requires only a small set of consecutive frames to perform, thus being able to process the video stream directly and produce results on the fly. We tested our implementation on standard available datasets, and compared the results with the most recent published work in the field. The results achieved show that our proposal produces summarizations that have similar quality than the best published proposals, with the additional advantage of being able to process the stream directly in the uncompressed domain.