With the nowadays information technologies, more and more smart sensors are deployed to monitor and survey our daily life. Instead of the centralized approach, sensor networks are evolving towards complete Cyber-Physical Systems, where sensing, processing and communications are spread throughout the city. This decentralization comes at a price though. Moving sensing, processing and communication capabilities to the edges, pose newer challenges for data processing, nodes management and system coordination.
Sma-RTy edge technologies find their natural deployment scenario in the Smart Cities vision. By distributing AI capabilities, the application can locally collect data and interact with users.
Edge AI technology has the following main results:
- Real-time processing at the edge
- Low latency interaction with users/customers
- Scalable deployment, from small to wide range use cases
- Low cost NRE. It involves embedded and self-consistent nodes.
- Reliability. It does not rely on central-star node communication but it can adapt to dynamic connectivity scenario
We are constantly working to address newer application challenges where edge processing and distributed artificial intelligence are key enabling factors to succeed.
You might find below few examples of very challenging applications from past and current projects.
People tracker and social distance monitoring
Our tracker algorithms have been currently modified to be adopted to the COVID19 monitoring measures. Since each node runs autonomously, we do not collect your personal data!
With Sma-RTy Edge technologies, you might:
- Easily scale the deployment to a wider application scenario.
- Plug and play deployment procedure.
- Provide customers user-friendly and intuitive interfaces to increase the citizens acceptance.
The Sma-RTy Edge technology can be applied to crowded place for advanced people flow monitoring. It can installed either indoor or outdoor and can adapt to any weather conditions.
Sma-RTy also proposes multi-spectral sensors to monitor ticket barriers and access platforms for real-time people flow evaluation. Such systems are particularly effective to support local authorities in managing high people density spaces. In particular, as for May 2020, COVID19 regulations introduced severe limitations for people in closed spaces. Sma-RTy surveillance systems may came at use to continuously enforce social distancing and people gathering policies.
AI-assisted driving trainer
The driving assistant is used to assist novice drivers when they first approach the driving practicing. A distributed network of smart camera autonomously analyze the vehicles trajectories to profile drivers with respect to their driving behaviors.
The Driving assistant exploits a set of roadside and cockpit camera to perform real-time analysis of the behavior of vehicles in terms of (i) safety, (ii) correctness of driving operations and (iii) ecoguide (i.e., actions aiming at limiting the ecological impact of the vehicle and optimizing its consumption).
By adopting this system, the driving instructor can follow the novice drivers from office without any risk. The driver will be constantly connected with the instructor during the entire driving lesson. Sensors data such as inertial measurement, depth maps and visuals collected eventually permit quantitative assessments of the driver profile.
In large parking slot area, cruising to find the good spot can be very frustrating. And it also increases unnecessary pollution to the ecosystem by having engine turned on moving low speed or queuing.
The Smart Parking application uses a joint analytical and artificial intelligence approach to monitor multiple parking slots and gather the details close to the waiting customers. As previously seen in other examples, the processing is completely done within the smart camera. This assures scalability and privacy preserving deployments.
You wish to know more about other applications, please drop us an email at firstname.lastname@example.org !