Introducing live video analytics from Azure Media Services—now in preview

Azure Media Services is pleased to announce the preview of a new platform capability called Live Video Analytics, or in short, LVA. LVA provides a platform for you to build hybrid applications with video analytics capabilities. The platform offers the capability of capturing, recording, and analyzing live video and publishing the results (which could be video and/or video analytics) to Azure Services in the cloud and/or the edge.

With this announcement, the LVA platform is now available as an Azure IoT Edge module via the Azure Marketplace. The module, referred to as, “Live Video Analytics on IoT Edge” is built to run on a Linux x86-64 edge device in your business location. This enables you to build IoT solutions with video analytics capabilities, without worrying about the complexity of designing, building, and operating a live video pipeline.

LVA is designed to be a “pluggable” platform, so you can integrate video analysis modules, whether they are custom edge modules built by you with open source machine learning models, custom models trained with your own data (using Azure Machine Learning or other equivalent services) or Microsoft Cognitive Services containers. You can combine LVA functionality with other Azure edge modules such as Stream Analytics on IoT Edge to analyze video analytics in real-time to drive business actions (e.g. generate an alert when a certain type of object is detected with a probability above a threshold).

You can also choose to integrate LVA with Azure services such as Event Hub (to route video analytics messages to appropriate destinations), Cognitive Services Anomaly Detector (to detect anomalies in time-series data), Azure Time Series Insights (to visualize video analytics data), and so on. This enables you to build powerful hybrid (i.e. edge + cloud) applications.

With LVA on IoT Edge, you can continue to use your CCTV cameras with your existing video management systems (VMS) and build video analytics apps independently. It can also be used in conjunction with existing computer vision SDKs (e.g. extract text from video frames) to build cutting edge, hardware-accelerated live video analytics enabled IoT solutions. The diagram below illustrates this process:


Use cases for LVA

With LVA, you can bring the AI of your choice and integrate it with LVA for different use cases. It can be first-party Microsoft AI models, open source or third-party models, etc.


Retailers can use LVA to analyze video from cameras in their parking lots to detect and match incoming cars to registered consumers to enable curb-side pickup of items ordered by the consumer via their online store. This enables consumers and employees to maintain a safe physical distance from each other, which is particularly important in the current pandemic environment.

In addition, retailers can use video analytics to...