AI use cases for a Smart City

Source: Deep Learning on Medium

So, my idea it was to find a way to short-circuit demand of parking with the offering, targeting the parking areas not delimited by barriers, choosing an approach as less invasive as possible, low cost, avoiding putting any kind of sensors to detect car presence on each parking lot. The other key factor was to find a channel, as easy as possible, to transmit info about the availability of free parking lots to the citizens.

Demo built is based on normal remote camera sending images taken every n seconds through a RasperryPi, on which I run a simple python code to take pictures sending to the cloud. In a real scenario, this plays the role of a normal low cost camera available on the market with minimum process capacity, put on an high pole.

Some customers have privacy violation risks concerns about sending images from a remote camera: these risks could be mitigated putting on the compute node attached to the camera a Python application with OpenCV leveraging its FaceRecognizer class to detect faces, and blur it, as well as License Plate.

The picture taken it is sent to the server on which a trained DNN detects the presence of a car in a parking lot.

I’ve addressed a scenarios in which we have fixed pre-defined parking lots. In this case the image is segmented to crop the 6 lots of demo and classify one by one. The original image is overlapped with 6 boxes, red or green, as you can see in the figure:

Image with cars detection

if it’s occupied or not, and a metadata occupation json file returned to the channel will provide the info: a chatbot.

In my case I’ve used Oracle Digital Assistant, the OCI service that provides the platform and tools to build easily an AI-powered assistants that is connected to the backend application that provide the parking lot status.

Chatbot conversation through Facebook Messenger

It uses artificial intelligence for natural language processing and understanding, to automate engagements with conversational interfaces that respond instantly, in my case Facebook Messenger. In the demo, citizen can interact in natural language to get several services, among which is parking availability.

As collateral, you can collect all data related to lots availability in an Oracle Autonomous DB, in order to gain insight about critical issues in particular time slot in order to plan better the offering in term of parking of a real Smart City.