Exploring the Techniques for Crowd Video Summarization: A Comprehensive Survey

Technologies for crowd video summarization aim to create an efficient and comprehensive summary by selecting the most significant parts of the video content. It aims to automatically generate a simplified version of an input video that contains only the most relevant or interesting parts. Numerous approaches have been developed in the past few decades and the state-of-the-art is exemplified by methods utilizing architectures. This research examines the current progress made in the field and presents a thorough overview of existing crowd video summarization methods. In this paper, we present our motivation for exploring the development of technologies for crowd video summarization and discuss the task, characteristics, and taxonomy of existing algorithms. We provide a systematic review of related literature and report on protocols for objectively evaluating crowd video summarization algorithms by comparing the performance of several approaches. The results of these comparisons, as well as some documented considerations about the amount of annotated data and appropriate evaluation protocols, are used to suggest possible research directions for future work.