Video Summarization Overview

TL;DR

This survey provides a comprehensive overview of automatic video summarization techniques, including traditional and deep learning approaches, evaluation methods, and open challenges in the field.

Contribution

It offers a detailed review of existing methods, benchmarks, and evaluation protocols, highlighting recent advances and future challenges in video summarization.

Findings

Deep learning has advanced video summarization effectiveness.

Evaluation protocols vary and have specific pros and cons.

Open challenges include improving summary quality and evaluation standards.

Abstract

With the broad growth of video capturing devices and applications on the web, it is more demanding to provide desired video content for users efficiently. Video summarization facilitates quickly grasping video content by creating a compact summary of videos. Much effort has been devoted to automatic video summarization, and various problem settings and approaches have been proposed. Our goal is to provide an overview of this field. This survey covers early studies as well as recent approaches which take advantage of deep learning techniques. We describe video summarization approaches and their underlying concepts. We also discuss benchmarks and evaluations. We overview how prior work addressed evaluation and detail the pros and cons of the evaluation protocols. Last but not least, we discuss open challenges in this field.