Video Processing And AI Detection System Architecture

This diagram shows how raw video is processed through frame extraction, object detection, and tracking to generate real-time alerts. It's useful for designing surveillance, safety monitoring, or computer vision applications. Tip: separate the real-time detection path from the storage/archival path since they often have different latency requirements.

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The prompt behind this diagram

Design a video processing and AI detection system architecture diagram showing a Video Input Source (camera feed), a Frame Extraction module sampling frames at set intervals, a Preprocessing step (resizing, normalization), an Object Detection Model (e.g., YOLO-style) identifying objects per frame, a Tracking Module linking detections across frames, an Event Detection Logic layer flagging specific conditions (e.g., intrusion, anomaly), an Alert Notification Service, and a Storage Layer archiving processed video and metadata. Include a Dashboard for viewing live detections and historical alerts.

Paste your own description (or Terraform / docker-compose / SQL schema) into draft1 and get a diagram like this for your exact system.

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