Scene Detection Policies and Keyframe Extraction Strategies for Large-Scale Video Analysis (2506.00667v1)

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.

• The paper introduces an adaptive policy-driven framework for efficient scene detection and keyframe extraction across diverse video types and lengths.
• Its methodology uses dynamic segmentation strategies based on video duration and a lightweight keyframe extraction module focused on perceptual features.
• Numerical results demonstrate effective performance on 120 varied videos, offering a scalable and integrable solution for large-scale video analysis applications.

Overview of Scene Detection Policies and Keyframe Extraction for Large-Scale Video Analysis

Introduction

Analyzing video content has become critical due to the surge in its production and consumption across multiple domains, ranging from media and education to surveillance and scientific research. This paper addresses the challenge of automatic scene segmentation and keyframe extraction, which constitute essential preprocessing steps in any video understanding pipeline. Traditional methods often lack generalizability, particularly when dealing with diverse video types and formats. This research introduces a unified and adaptive framework that operates efficiently across different video lengths and structures, ensuring enhanced scalability and robustness.

Methodology

The research presents a dynamic policy-driven approach to segment scenes and extract keyframes. The methodology uniquely tailors segmentation strategies based on video duration, employing adaptive thresholding for short clips, a hybrid approach for medium-length content, and interval-based segmentation for long durations. This adaptability enables efficient segmentation across heterogeneous video domains, allowing the system to maintain computational efficiency without sacrificing the accuracy of scene detection.

Keyframe extraction is managed through a lightweight module focusing on perceptual sharpness and brightness, thus avoiding deep saliency models and ensuring high throughput. This strategy is crucial for applications that demand transparency and computational efficiency, such as vision-language embedding and visual inspection.

Numerical Results and Validation

The paper provides a comprehensive evaluation across 120 videos from various categories, such as short clips, lectures, documentaries, and more. The results demonstrate effective segmentation and reliable keyframe extraction, with high keyframe coverage across different content types. The adaptive system achieves a balanced trade-off between scene granularity and computational load, proving its potential in production environments.

Implications and Future Directions

From a practical perspective, this research offers a scalable solution for video analysis systems, seamlessly integrating into existing architectures. It can significantly impact industries relying on automated video processing, with potential applications in video tagging, summarization, search, and editing. The release of publicly accessible inference services could further enable integration into decentralized or edge environments, emphasizing the approach’s versatility.

Theoretically, the framework sets a precedent for combining perceptual models with dynamic policy strategies, paving the way for future developments in adaptive video understanding systems. Proposed future enhancements include incorporating audio-aware segmentation, hierarchical scene structuring, and reinforcement learning-based keyframe selection, which could refine the system's accuracy and broaden its application scope.

Conclusion

This paper contributes a well-structured, adaptable framework for scene detection and keyframe extraction that promises extensive utility across various domains of video content analysis. Its implementation ensures ease of integration while maintaining high performance and interpretability. Continued development along the lines suggested could further enhance its capability, establishing it as a cornerstone technology in video understanding disciplines.