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Sunday 24 July 2022

Real-time video analytics with OpenVINO on Azure IoT Edge


In this blog post, I will go through how we can build real-time video analytics on IoT Edge devices with OpenVINO.

Before I start, let me explain what is OpenVINO and what is good for?

OpenVINO stands for Open Visual Inference and Neural network Optimization.

As its name suggests, it is used to optimize the models and allows you to host the models based on your process architecture.

There are two main steps given below:

1.       Convert your favorite model to IR format as shown below:

2.       Host your IR (intermediate representation) to the OpenVINO model server. The runtime is process architecture dependent.

With this approach, you can develop your own model with your favorite framework or download the prebuilt models from Model Zoo and host them in OpenVINO.

You can

Now we know the OpenVINO, let’s see how we can use it for IoT Edge. We can host the OpenVINO model in a Docker container which makes it perfect for IoT Edge devices. The below diagram shows the general architecture of how OpenVINO can be used:

For more information on how OpenVINO can be used with Azure IoT Edge please visit

Now let’s see how we can host the model and how we can consume it to make inference for given video frame. For the example, I have chosen a prebuilt model called “Vehicle Detection” from Model Zoo.

Run below command to host model in a Docker container:

$ docker run --rm -d -v "path/to/your/models":/models:ro -p 9000:9000 -p 9001:9001 openvino/model_server:latest --config_path models/config.json --port 9000 --rest_port 9001

You can see OpenVINO documentation for more information:

Once the model is hosted you can query the metadata of the model by navigating to the URL

http://iotedgedevice-ip:9001/v1/models/vehicle-detection-0202/versions/1/metadata and response will be as shown below:

Which shows the shape of input and output. It means our vehicle detection model is hosted.

Now let’s consume this model from the python script (which later will be converted to the IoT Edge module to process the frame).

The first step is to load the frame in the OpenCV object as shown below:

The endpoint for model prediction is at http://iotedgedevice-ip:9001/v1/models/vehicle-detection-0202/versions/1:predict

We need to submit this image as JSON data and get the result as JSON which contains a NumPy array as per the specification mentioned at

The below method is used to convert an image to JSON input for the model:

The code flow is shown below:

Here is the model that predicted the vehicle bounding boxes which are then drawn to the mage as shown below:

We can see the model predicted all those vehicles shown in the above image.

That’s it for now and thanks for reading my blog.



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