Video Summarization Using Deep Learning and Optimization Approaches: A Systematic Review

The burgeoning volume of video data has intensified the imperative for advanced mechanisms to enable efficient storage, navigation, indexing, retrieval, and fluid content dissemination. Despite extensive scholarly efforts in video summarization, there persists a critical need to consolidate recent innovations, delineate ongoing challenges, trace emerging paradigms, standardize evaluative frameworks, and curate benchmark datasets for rigorous performance appraisal. This survey provides a comprehensive analysis of contemporary summarization methodologies, spotlighting transformative advancements and paradigmatic shifts over the past two decades that have redefined the domain. It systematically classifies core approaches, synthesizes pivotal insights, and underscores significant milestones. Video summarization condenses voluminous footage into its most semantically rich segments, a functionality indispensable for applications such as surveillance, where continuous Closed-Circuit Television (CCTV) monitoring underpins security and incident tracking. Yet, processing protracted video content remains computationally demanding and time-intensive, a challenge compounded when integrating multiple perspectives, thus emphasizing the centrality of Multi-View Summarization (MVS). This study elucidates the theoretical underpinnings, technical intricacies, and practical implications of both single-view and multi-view summarization, with particular emphasis on deep learning architectures and optimization-driven strategies. Through a systematic review of recent developments, the paper aims to inform future research, unlock new opportunities, and contribute to the evolution of more robust and adaptive video summarization frameworks.