One of the big new features of Gravio 4 is the way you can create inference files to use Artificial Intelligence and Machine Learning to detect objects in visual feeds, such as from CCTV security camera systems based on ONVIF.
Please refer to the ONVIF section of this documentation to learn how to connect your network camera to Gravio 4.
Please note that you need a Gravio Enterprise Software License to create and deploy your own image inferences within your Gravio Edge IoT infrastructure.
Once you have the possibility to create your own image inference files and teach Gravio to detect anything you want it to recognise in a picture, the possibilities are only limited by the quality of the picture of your camera and your learning model accuracy.
In this section, we learn how to create our own inference models. In essence, Gravio 4 leverages the power of Google’s TensorFlow framework. This means you can use Google’s Cloud AutoML vision machine learning model builder for image recognition creation. You can train the models on Google Cloud.
Building your models is quite straight forward. Google provides a good beginners guide.
Once your model is created, you can upload it to your Gravio Coordinator, from which your Gravio Edge Nodes can deploy the inference files locally and therefore interact with the local camera systems. To deploy such a model, please consult your Gravio Coordinator documentation.