Live Video Analytics

Quite often new and innovative solutions require at least some technical effort. IOT systems e.g. need to be deployed , calibrated, provisioned with network and power, which requires quite some effort.
Depending on the use case Live Video Analytics, a feature of Azure Media Services, might be able to reduce this effort. All you need is a camera and, ideally, an IOT Edge device connected to it. This is especially helpful in dynamic environments, such as delivery entrances, machine ports or storage racks, where a lot of different things are going on simultaneously. In these dynamic environments dedicated sensors are often hard to calibrate and locate.
Video Analytics use AI models to detect motion, things and can even go down to detecting and reading text, such as numbers on license plates, addresses on parcels, etc. .
Microsoft describes some of the interesting possibilities and scenarios quite good in this recent blog post.

Detect workers and cargo in a video stream

What I like is that the AI video analytics models can be run on an edge device. This saves a lot of bandwidth and also keeps your eventually sensible video material on-premise! There are models available for use, but you can also build custom ones and thus create, adapt and fine tune the detection for a use case. Existing video streams also can be used for processing, which, in some cases, enables you to start right away focusing on implementing the IOT Edge solution,
As the analytics models are able to create events to be consumed by an EventHub, they can be used as a publisher of triggers to build business solutions on.
Use Azure Serverless capabilities and you have a sophisticated video analytics system for your use case up and running in days, or even only hours.


Real-time Information Push with Serverless SignalR

Quite often there are situations, where one needs dynamically changing information proactively delivered, as fast as possible.
Stock market info is the poster child here, but there are quite a few other day-to-day use cases that also require close to real-time display of relevant data on many screens and across locations. Traffic information, factory line status, logistic tracking are to name as samples here.

Looking at the Microsoft technology stack, SignalR is the tool of choice to tackle these requirements.
SignalR leverages several different approaches such as WebSockets, Server Sent Events or Long Polling, transparently for developers in the background, to deliver information needed in the best way possible. All based on the connectivity scenario / quality of a client application.

It is great to see that SignalR is now provided in “Serverless Mode” by Azure SignalR Service, which takes away scaling and infrastructure maintenance effort for this service from developers.

Following the very efficient “low code/serverless” approach, I am always propagating, I want to recommend having a look at this interesting sample, showing how to-do real-time communication leveraging the Serverless SignalR Service in combination with Azure Functions.

Take it from me, SignalR is a great and fun technology to work with!