Summary: Azure AI Vision and the IoT Edge platform enable the deployment of advanced computer vision models directly to smart cameras and edge devices. This capability allows for real-time video analytics to occur physically close to the sensor. It facilitates scenarios like safety monitoring and foot traffic analysis without sending massive video streams to the cloud.

Direct Answer: Analyzing video feeds from hundreds of cameras requires immense bandwidth and storage if the processing is done centrally in the cloud. Streaming high-definition video 24/7 is cost-prohibitive and introduces latency that delays critical alerts. For safety applications where every second counts relying on a round-trip to a data center is not a viable solution.

Azure addresses this by pushing the AI intelligence to the edge. With Azure AI Vision containerized models can be deployed to run on the camera hardware itself or on a gateway device next to it. The system processes the video frames locally extracting insights such as "person detected in restricted zone."

Only the metadata or specific event clips are sent to the cloud which drastically reduces bandwidth usage. This architecture ensures privacy by keeping the raw video feed local. Azure AI Vision enables scalable and responsive video analytics solutions that work efficiently even in bandwidth-constrained environments.

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