A Comprehensive Survey of Video Summarization Techniques with Emphasis on Real-World Applications

Abstract

The rapid growth of digital video content in many areas has created an urgent need for advanced methods that can quickly and effectively extract important information from long videos. Video summarization means making a short video that keeps all the important parts and main ideas of a long video. This helps people save time and quickly understand the video without watching it all. Recent advances in deep learning and computer vision have led to better tools that can automatically create these summaries by removing repeated parts, understanding the content, and personalizing summaries for different viewers. This paper reviews different video summarization methods, shows how well they work, and introduces their strengths and weaknesses. The performance of these methods is assessed on various benchmark datasets. Their effectiveness is evaluated using metrics such as precision, recall, F1-score, and diversity. Moreover, their respective strengths and limitations are discussed in terms of scalability, real-time processing, and adaptability to different video domains. "How to measure if a summary is good" is another issue that is explained, highlighting key challenges in the field, such as handling long videos and making summaries that fit user preferences. This research will benefit researchers seeking to advance or apply video summarization techniques across scientific and technical domains.