Edge AI Safety Pipeline Deployment

I already have a working proof-of-concept that analyses live camera feeds, but it needs to mature into a robust edge-ready pipeline. The core mission is clear: reliably detect vehicles and traffic hazards on roads, intersections, and parking areas while also flagging human-centric emergencies such as falls, fighting, or people lying on the ground. Here is what I need from you: • Review the current architecture and models, then recommend concrete improvements that boost accuracy and lower latency on resource-limited edge devices. • Streamline the full pipeline—from video ingestion through inference to alerting—so it can scale across multiple cameras without cloud dependency. • Package the solution for straightforward deployment (Docker or similar containerisation is preferred) with configuration files that make adding new cameras effortless. • Provide concise documentation and a demo showing real-time detections and how alerts are surfaced (REST, MQTT, or WebSocket—whatever best fits the design you propose). Acceptance criteria: 1. Vehicle and traffic-hazard detections remain consistently above the precision/recall levels we agree on during kickoff. 2. Falls/fighting/person-down events are recognised within a reasonable time window and verified through sample test clips. 3. End-to-end latency per stream stays within edge-device limits while supporting at least two concurrent 1080p feeds in the demo. 4. Deployment steps can be followed by someone with basic Docker experience and succeed on first pass. If you are confident optimising computer-vision models (e.g., YOLO, EfficientDet, TensorRT) and orchestrating them in lean containers, I’d love to see how you can elevate this system.