Imagine the safety control room at the heart of a bustling public space like a busy train station. A pair of employees is scanning a handful of monitors that display rotating frames from cameras positioned around the property. But that means precious minutes can tick by before a specific view pops back up; if an incident happens in the meantime, this lag allows it to go unnoticed until the image reappears and alerts the control room. By then the damage could be done.
Now, picture this control room in a “smart city,” with AI technology that uses object detection to surface a potential issue just as it arises. By notifying the team immediately, they can take charge and address the issue before it escalates. Crisis averted.
AI Technology Helps Identify and Measure Risk
That’s the promise of Sensing Feeling, a specialist in computer vision, machine learning, and AI. The company’s SensorMAX platform proactively detects and prevents incidents, using existing infrastructure, such as the station’s closed-circuit television cameras.
“We add value to the cameras or visual technology that a customer has already invested in by dramatically improving its performance,” says Jag Minhas, CEO and founder of Sensing Feeling. “Instead of having to make resource-intensive decisions, like adding more people or screens in order to provide a safer environment, our solution uses software and AI technology to identify the risks for them.”
The AI software will process the feed from every camera connected to the system and give the control room operators a dynamic, real-time assessment of each zone’s risk, based on a repertoire of pre-trained models combined with behavioral analytics.
Each client determines the events that it believes reflect risk in their particular scenario, and Sensing Feeling uses that information to attach a “risk index” to every camera. For example, in a train station in a smart city, that could mean the software has sensed an unaccompanied child; a crowd that is developing at a time of day when it normally wouldn’t or is running in panic; or a person riding a bike against regulations.
Given the limited number of displays in a traditional control room, SensorMAX then uses its AI-powered software to prioritize what the client is most interested in or worried about, and ensures that the riskiest camera zones are being displayed. “We surface on their screens where we believe the highest risk is playing out at any moment in time,” Minhas says. “In this way, it can improve an existing system by offering a live indication of risk as it’s developing so you can potentially de-escalate situations, where before you would have had to rely on a postmortem approach.”
Of course, public safety is at its heart, but using AI software proactively has a financial benefit for the customer, as well. Preventing incidents rather than reacting to them allows clients to avoid costs that could otherwise arise.
.@sensingfeeling adds value to the visual technology that a customer has already invested in by dramatically improving its performance.
Partnering With Industry Leaders
To bring its solutions to the marketplace, Sensing Feeling works with channel partners that already have sector experience. “As a group of AI and ML experts, we find our most successful business development activities come through partnering with someone who understands what SensorMAX can offer, and then is able to package it to add value to a solution they’re already selling to an end user,” Minhas says.
For example, the company might join with a design firm that can use information collected by SensorMAX to identify motion paths and clustering behavior in commercial buildings. These measurements allow them to understand how people gather to better plan space to promote productivity and collaboration—or change traffic flows to meet new COVID-related regulations.
With an architecture based on the Intel® OpenVINO™ Toolkit, the versatile SensorMAX technology can be applied to a wide range of situations in a variety of different industries—beyond smart city, office building, or public space applications.
Other notable use cases Minhas cites include reducing accidents in the oil and gas industry or alerting managers to early signs of stress and fatigue among manufacturing workers. “Its practicality extends to any customer who wants to improve user experience and enhance an environment by managing safety and risk, while preserving privacy,” Minhas says.
Ethical Mandate at Its Core
Personal privacy is top of mind today—as it should be—and Minhas emphasizes that the system’s architecture is designed with that as a priority. As he explains, the system uses sensors, rather than surveillance cameras, which allows it to process data, but not record, store, stream, or transmit images. That means that Sensing Feeling doesn’t track specific activities or any biometric profiling, such as facial recognition, that would identify an individual.
“Our tagline is ‘ethical by design,’ and we aim to uphold that prominent position,” he says. In fact, they encourage their customers to be transparent with end users about how and why the technology is being deployed. Minhas adds, “The reality is that if you can’t communicate a benefit, then we don’t think our solution should be adopted at all.”