Video Synopsis Algorithms and Framework: A Survey and Comparative Evaluation

Abstract

With the increase in video surveillance data, techniques such as video synopsis are being used to construct small videos for analysis, thereby saving storage resources. The video synopsis framework applies in real-time environments, allowing for the creation of synopsis between multiple and single-view cameras; the same framework encompasses optimization, extraction, and object detection algorithms. Contemporary state-of-the-art synopsis frameworks are suitable only for particular scenarios. This paper aims to review the traditional state-of-the-art video synopsis techniques and understand the different methods incorporated in the methodology. A comprehensive review provides analysis of varying video synopsis frameworks and their components, along with insightful evidence for classifying these techniques. We primarily investigate studies based on single-view and multiview cameras, providing a synopsis and taxonomy based on their characteristics, then identifying and briefly discussing the most commonly used datasets and evaluation metrics. At each stage of the synopsis framework, we present new trends and open challenges based on the obtained insights. Finally, we evaluate the different components such as object detection, tracking, optimization, and stitching techniques on a publicly available dataset and identify the lacuna among the different algorithms based on experimental results.