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SophyAI, a careful observer of what happens in the areas where vending machines are located …… and more.
By means of normal cameras it is possible to check the number of presences and their spacing by generating warnings if there are gatherings or too close distances between people. SophyAI also provides and historicizes the flow of people data which can be very useful for analyzing the congruity between people present and the sales made. The infrastructural intervention is minimal and, thanks to SophyAI’s ability to interface with any IoT, the same vending machines can be connected to it for optional control and activation functions.
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SophyAI and its Cognitive Engines
The SophyAI Artificial Intelligence platform with its 3 neural “engines” allows having in a single suite of A.I. functions of deep learning, georeferencing, and workflow. COGITO allows applying the artificial intelligence to complex events that become usable, to the final user, in their classification and interpretation, making known and precise their spatial position and generating specific activities of information and active control that allow emulating the actions that a skilled human observer would do. This happens without solution of continuity. SophyAI doesn’t get tired and doesn’t get distracted.
Realtime 3D reconstruction using SFM algorithms.
Localization and Mapping SLAM for 3D recostruction , realtime localization and obstacle advoidance. Typical application is autonomous navigation.
Machine Learning CNN technology for realtime video classification , localization and scenario description
A.I realtime pose detection estimation .
A.I. realtime DPI detection .
Realtime geo localization of object view in the scenario by standard camera and classify by CNN neural network.
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How SophyAI® works (A.I. & Robotic Platform)
The SophyAI’s neural network can “classify and interpret” if properly trained, a scenario caught from a normal video surveillance camera.
The streaming video, coming from the cameras, are sent to the neural network that, through specific algorithms, identifies particular “objects” of the scene.
In addition to this, a specific geolocation module can identify the correct spatial position of the observed “objects” and transform it into geographic coordinates, representable on any map.
The third component of Cogito, the workflow module, takes care of implement predefined “actions” in relation to the behavior of the classified “objects”. This is a very important function of the system, in fact it allows SophyAI to relate to other systems providing information and commands
Quantity, direction, velocity, and state of “objects” are processed by the workflow module to send information and alerts to operators.
Cogito can also be interconnected with collaborative elements (IOT) present in the observed area, both to improve his understanding of the scene and send automatic commands as: close and open gates, activate fire-fighting systems, change traffic light timing, etc.
The street lighting analyzed by SophyAI®
Through Cameras It is possible to define the operating state of a city lamppost using the “smart” analysis of the light spots.
The neural network can identify if a lamp is working or not.
The localization module defines the spatial position of the lamp failure and places it on a map. The workflow module immediately sends an alert to the maintenance staff and the correct position of the out of order lamp.
This is very useful to know exactly where the failure is located (repairs are not always made immediately, and it is very convenient, for maintainers, know the exact geographic location of the fault, which can be even detected when the lighting network is off, as in daylight hours).
Based on its positioning, a single camera can keep under control different streetlights.
This solution is particularly suitable for those city areas where there are no light poles connected to the network, and where there are no sensors that can determine the lamps status. The SophyAI system allows to significantly reduce the infrastructural costs obtaining substantial advantages for maintainers and citizen.
If the street lampposts are connected to a network and they are equipped with dimmable lamps, they can become a “collaborative” objects (IOT) and the SophyAI’S workflow module can intervene in modulating the light intensity in according to the vehicles or pedestrians presence in the street. This can allow the municipality to substantially reduce the energy expenses for street lighting service.
#INFRASTRUCTURE MANAGMENT
How SophyAI® works (A.I. & Robotic Platform)
The SophyAI’s neural network can “classify and interpret” if properly trained, a scenario caught from a normal video surveillance camera.
The streaming video, coming from the cameras, are sent to the neural network that, through specific algorithms, identifies particular “objects” of the scene.
In addition to this, a specific geolocation module can identify the correct spatial position of the observed “objects” and transform it into geographic coordinates, representable on any map.
The third component of SophyAI, the workflow module, takes care of implement predefined “actions” in relation to the behavior of the classified “objects”. This is a very important function of the system, in fact it allows SophyAI to relate to other systems providing information and commands
Quantity, direction, velocity, and state of “objects” are processed by the workflow module to send information and alerts to operators.
SophyAI can also be interconnected with collaborative elements (IOT) present in the observed area, both to improve the understanding of the scene and send automatic commands as: close and open gates, smart parking management, activate fire-fighting systems, change traffic light timing, etc.
Smart Traffic by SophyAI®
Video surveillance cameras can allow SophyAI to interpret and analyze the city’s traffic condition and provide, in addition to useful information on its density by areas, timely indications of what happens in the traffic dynamics. The neural network, as already explained, can define the spatial conditions of what has been classified, in sense of directions and speeds. This information, with the appropriated algorithms, can be used for interpreting the traffic status (vehicles and persons) in various manners.
For example, in many cases the traffic lights timing is not appropriate to the real traffic conditions, SophyAI may decide to change the traffic lights synchronization to perform a better traffic management, giving priority to the road line with more vehicles. If a vehicle stops, improperly or for failures, in the driving lane, the neural network can be instructed to analyze the state of vehicles movement and interpret whether it can generate danger or not. In the event of traffic, SophyAI interprets slowdowns or stops as a normal condition, but if the neural network does not detect traffic and recognizes that a vehicle is stationary in a predetermined area (driving lane) for a certain period (higher than that due for Example to a normal parking), can generate appropriate alarms. The workflow module immediately can send an alarm and eventually act on the traffic light to prevent accidents.
SophyAI can carry out a precise counting of the vehicles passing and classing them in relation to the type of the vehicle itself. In the same way SophyAI can count people Beyond the value of statistical counting, this function can be very useful if interfaced with collaborative sensors (IOT) as traffic light network or smart devices used by municipal police.
#INFRASTRUCTURE MANAGMENT
How SophyAI® works (A.I. & Robotic Platform)
The SophyAI neural network can “classify and interpret” if properly trained, a scenario caught from a normal video surveillance camera.
The streaming video, coming from the cameras, are sent to the neural network that, through specific algorithms, identifies particular “objects” of the scene.
In addition to this, a specific geolocation module can identify the correct spatial position of the observed “objects” and transform it into geographic coordinates, representable on any map.
The third component of SophyAI, the workflow module, takes care of implement predefined “actions” in relation to the behavior of the classified “objects”. This is a very important function of the system, in fact it allows SophyAI to relate to other systems providing information and commands
Quantity, direction, velocity, and state of “objects” are processed by the workflow module to send information and alerts to operators.
SophyAI can also be interconnected with collaborative elements (IOT) present in the observed area, both to improve the understanding of the scene and send automatic commands as: close and open gates, smart parking management, activate fire-fighting systems, change traffic light timing, etc.
Smart Parking by COGITO®
One or more cameras, placed on lamppost or on a building (with adequate altitude), can view and interpret the situation of parking lots in a very dynamic way. The neural network geolocates the parking lots and categorizes them. SophyAI, in real time, understands if it is busy or free, can
count the vehicle’s permanence time and define its typology (car, truck, motorbike).
In the installation phase, the neural network will be appropriately instructed to minimize inconsistencies and false positives in narrow roads ( mainly due to the changing of projected buildings shadows in a bright sunny day).
The collected information, give the workflow module the capability to send notices on available free parking lots, with correct location, to the citizen’s smartphones (to that end, a specific App can be customized for the Municipality) .
All the information, with time stamp, elaborated by SophyAI, can be utilized by the Municipality for specific services like automatic parking billing (we have experience in creating “smart contract” with blockchain that “certify” both the payments and can “notarize” events occurred and recorded in the parking area like a damaged or stolen vehicle).
The solution is a winner compared on traditional automated parking controls, not requiring any installation on the ground with consequent strong costs reduction. A smart parking area can be set up in a very short time.
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