Deep Learning-Driven Video Summarization on the Cloud: A Pathway to Efficient Storage and Quick Access

Cloud-based video summarization is an increasingly popular technique for managing large video repositories more efficiently. In this research, we present a novel approach for cloud-based video summarization that focuses on reducing storage costs and improving accessibility by automatically extracting critical information from videos and generating brief summaries. Our approach leverages cloud computing technology and deep learning algorithms for video analysis and processing. We evaluate the effectiveness of our approach using a real-world dataset of video files and demonstrate that it can generate high-quality video summaries while significantly reducing storage requirements. Our proposed approach has potential applications in areas such as security monitoring, video surveillance, and multimedia content management. This paper contributes to the existing literature by presenting a comprehensive study of cloud-based video summarization and proposing a novel solution that addresses the limitations of existing approaches, thereby improving the efficiency and cost-effectiveness of video management.