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

TL;DR

This paper introduces a versatile, adaptive framework for scene detection and keyframe extraction in videos, capable of handling diverse formats with high efficiency and applicability across multiple domains.

Contribution

A unified, adaptive system for video scene segmentation and keyframe extraction that adjusts strategies based on video length and content, improving generalizability and processing efficiency.

Findings

Successfully deployed in a commercial platform across various domains.

Handles diverse video formats with consistent granularity.

Achieves high-throughput processing suitable for large-scale analysis.

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.