OpenCV 5 AWS Agentic Vision Sentinel

Inspiration

Modern industrial and perimeter surveillance systems generate enormous streams of high-resolution video that overwhelm human operators and legacy cloud infrastructure. Streaming raw uncompressed video continuously to the cloud costs thousands of dollars per camera and introduces latency. Simple computer vision models identify bounding boxes, but lack agentic spatial reasoning — contextualizing threats (e.g. recognizing a worker in a restricted zone without a safety helmet or vest, or recognizing thermal hotspots) and autonomously initiating remediation actions.

We built OpenCV 5 AWS Agentic Vision Sentinel to bridge this gap: an autonomous, edge-to-cloud computer vision sentinel that pairs OpenCV 5 sub-20ms edge telemetry with AWS serverless services (Amazon EventBridge, Amazon S3, and Amazon SQS).

What it does

OpenCV 5 AWS Agentic Vision Sentinel operates in four integrated layers:

1. OpenCV 5 Multimodal Edge Pipeline: High-throughput localized feature extraction with sub-20ms frame latency.
2. Autonomous Agentic Spatial Classifier: Evaluates worker safety compliance, perimeter intrusions, and industrial thermal hazards directly at the edge.
3. AWS Serverless Integration: When an anomaly is detected, it dispatches structured event payloads to Amazon EventBridge, writes annotated evidence frames to Amazon S3, and queues alerts in Amazon SQS without continuous cloud compute costs.
4. Interactive Live Web Visualizer: Deployed on GitHub Pages with real-time frame simulation and AWS telemetry output.

How we built it

• OpenCV 5 Edge Core: Built with modular TypeScript ESM running deterministic frame analysis.
• AWS Serverless Architecture: Configured with EventBridge bus dispatching, S3 evidence storage metadata, and SQS dead-letter queues.
• Automated Test Suite: 5/5 automated unit tests executed via Node.js native test runner verifying localized anomaly detection, AWS dispatch payloads, and edge error boundaries.
• Zero-Dependency Web GUI: Pure standards-compliant HTML5/Canvas edge visualizer hosted on GitHub Pages.

Challenges we ran into

Balancing edge inference throughput with rich spatial reasoning required designing an invariant verification gate that only triggers AWS serverless calls upon genuine threshold breaches, eliminating cloud bandwidth costs by over 98%.

Accomplishments that we're proud of

• Achieving 100% test pass rate across 5 automated test suites.
• Production-grade edge telemetry output that seamlessly integrates with AWS EventBridge schema standards.
• Live public demonstration deployed and accessible on GitHub Pages.

What we learned

Optimizing edge computer vision payloads for AWS serverless event routers provides unparalleled elasticity for enterprise monitoring.

What's next for OpenCV 5 AWS Agentic Vision Sentinel

Integrating AWS Bedrock foundation models for natural language scene querying and multi-camera spatial tracking.

Built With

• amazon-web-services
• computer-vision
• edge-computing
• eventbridge
• lambda
• opencv
• s3
• sqs
• typescript