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Stream It, Inc.: Seeing the Unseeable
Stream It, a Nevada-based real-time video surveillance and analytics platform provider, takes a unique approach to smart cities. All other smart city solutions embarked on their journey to solve the easiest challenge—networking low-bandwidth networkable devices and sensors, such as parking and traffic sensors together. But those solution providers and the cities they served quickly found that this approach is unable to provide a holistic view of the city they needed. So, they had to change their architecture multiple times. Conversely, Stream It realized very early that “no city is truly smart if it is blind to live mobile video and analytics,” mentions Lance King, co-founder, Stream It. So, it solved the hardest problem first by creating a mobile-capable, real-time mobile analytics platform. For example, cities need live video from their buses, trains, and police body cameras rather than just traffic cameras, to have true situational awareness.
However, simply having the correct architecture was not enough. Despite all this new data and advanced capability for gathering real-time analytics, a haunting reality had set in—the vast community of IoT pundits and prognosticators had missed a key aspect of intelligence in the emerging AI economy. Don’t misunderstand this assertion. IoT devices, as we know them today, can be arrayed to provide useful benefits and solutions to complex problems. But when it comes to accomplishing the objective of making a city “smart,” IoT sensors are not the answer. As Bill French, Co-founder, Stream It, says, “IoT sensors (alone) have almost zero chance of making cities smart.”
Setting aside the cost and complexity of deploying massive quantities of IoT sensors dedicated to discrete measurements, you must capture vast amounts of data, then analyze and assess it. This will require massive AI processing to reach plateau intelligence (i.e., a collective consensus of awareness in real-time) for any given observation.
Something’s Got to Give
More precisely, something has got to change. The IoT IP-device-everywhere mantra is a little unintelligent and perhaps unsustainable. To be clear, the benefits of IoT are good. The problem is that we need millions of IoT devices to make complex assessments. A moderate city could require millions of IoT sensors to become even slightly more intelligent.
French correctly observes, “... the answer to realizing the potential for a truly smart city is to recognize that IoT must transition from physical devices to virtual devices.” How is that possible?
Stream It, a pioneer in the domain of IoR, has developed a comprehensive and scalable approach to seeing the unseeable
The pathway to building smarter cities is clear. Machine vision, coupled with relatively straightforward AI models and machine learning, is how we can virtualize the IoT and transition to the Internet of Recognition (IoR).
Where there are hundreds of traffic-sensing devices and physical probes consuming massive amounts of connectivity resources and maintenance, there will soon be far fewer AI-enhanced high-resolution cameras— each running dozens of embedded virtualized sensors detecting traffic flows, accidents, crime, deceptive activities, and more—providing far more value for less money.
Where there are presently tens of thousands of physical IoT devices planned for monitoring entertainment facilities and public safety, the role of those IoT devices (and many more capabilities) will be compressed into machine-learning software that employs vision to see the unseeable.
In simplest terms, every virtualized IoR sensor can replace dozens of IoT sensors. Genuinely, smart cities are finally possible. This is where Stream It is in a class by itself. The company provides an edge-first real-time video surveillance and analytics platform enabling customers to use powerful artificial intelligence algorithms in real-time to enhance security and improve safety for mobile and stationary environments. Stream It, a pioneer in the domain of IoR, has developed a comprehensive and scalable approach to seeing the unseeable.