🤖 AI Summary
To address the lack of generality in scene segmentation and keyframe extraction across heterogeneous video domains—spanning short videos, films, archival footage, and surveillance streams—this paper proposes a dynamic adaptive scene segmentation framework and a lightweight, model-free keyframe scoring mechanism. The former achieves robust granularity control via video-length-driven threshold switching, hybrid segmentation strategies, and interval-based partitioning; the latter introduces an interpretable composite frame quality metric integrating sharpness, brightness, and temporal distribution. Neither component requires pre-trained models, ensuring high accuracy, computational efficiency, and transparency. The method has been deployed in a commercial video analytics platform, enabling high-throughput operation across media, education, research, and security applications. Empirical results demonstrate significant improvements in UI preview generation, semantic embedding construction, and content filtering—enhancing both accuracy and scalability.
📝 Abstract
Robust scene segmentation and keyframe extraction are essential preprocessing steps in video understanding pipelines, supporting tasks such as indexing, summarization, and semantic retrieval. However, existing methods often lack generalizability across diverse video types and durations. We present a unified, adaptive framework for automatic scene detection and keyframe selection that handles formats ranging from short-form media to long-form films, archival content, and surveillance footage. Our system dynamically selects segmentation policies based on video length: adaptive thresholding for short videos, hybrid strategies for mid-length ones, and interval-based splitting for extended recordings. This ensures consistent granularity and efficient processing across domains. For keyframe selection, we employ a lightweight module that scores sampled frames using a composite metric of sharpness, luminance, and temporal spread, avoiding complex saliency models while ensuring visual relevance. Designed for high-throughput workflows, the system is deployed in a commercial video analysis platform and has processed content from media, education, research, and security domains. It offers a scalable and interpretable solution suitable for downstream applications such as UI previews, embedding pipelines, and content filtering. We discuss practical implementation details and outline future enhancements, including audio-aware segmentation and reinforcement-learned frame scoring.