Unlocking video insights at scale with Amazon Bedrock multimodal models

This article demonstrates how Amazon Bedrock's multimodal models enable scalable video analysis through three architectural approaches, each optimized for different use cases and cost-performance trade-offs.

• Frame-based workflow: Samples frames, removes duplicates, extracts visual insights for surveillance and compliance monitoring
• Shot-based workflow: Segments videos into clips, captures temporal context for media production and content cataloging
• Multimodal embedding: Enables semantic video search using natural language queries and visual similarity matching
• Smart frame deduplication: Uses Nova embeddings or OpenCV ORB to reduce processing costs while preserving visual information
• Video segmentation options: Scene detection for narrative content or fixed-duration for continuous recordings
• Serverless architecture: Built on Step Functions, Lambda, DynamoDB, and S3 for scalability and cost-efficiency
• Detailed cost tracking: Provides token usage and cost breakdown by model type for informed decision-making
• Real-world examples: IP camera monitoring, media chapter analysis, and social media content moderation

The solution provides flexible, scalable video understanding without requiring specialized computer vision expertise, with open-source code available on GitHub for immediate deployment.