The field of computer-aided vision and image recognition took a significant step forward in 2012. And since then, it has become apparent that computer-aided vision technology has established a permanent cornerstone in the future of software applications.
How this relates to the Internet of Things (IoT) is so obvious that few have given it a second thought. And even fewer have considered the emerging prospects of the Internet of Recognition™ - an idea based on pervasive machine vision.
Our basic understanding of IoT predicates that devices of many types will soon have IP addresses and the capacity to communicate with other devices and applications over networks. Indeed, it is our fundamental understanding that members of the IoT must possess embedded electronics to participate as bona-fide “things” on the network.
This is a rational if not a narrow definition of IoT. Reliable, automated image recognition is about to change that definition.
IoT and image recognition share a common goal.
- IoT's primary purpose is to create a bond between the physical world and digital space.
- Image recognition events create virtual representations of actual physical objects - a virtualization of "things".
The subtle relationship between objects that can be recognized without human assistance and IoT is as elegant as it is invisible.
Physical objects that are recognized and identified through computer-aided vision are magically bound to the digital world.
In some cases, physical objects of an image recognition event are homogeneous - i.e., a Tide laundry logo on a race car, a bus advert, or perhaps the logo on an actual box of Tide detergent.
But it's also possible that certain recognition events may contain uniquely identifiable artifacts. For example, a medical device whose serial number is legible in the image, or the image of a vehicle with a license tag.
Image recognition events fall into these two basic categories with the former a prime candidate for aggregate analytics, and the latter serving as the basis for discrete identity, i.e., a unique member in the emerging internet of things.
Homogenous Recognition Events
This type of recognition event possesses properties that are likely to be useful in aggregate contexts. Indeed, the recognition of a class of automobile parts which have failed, establishes a place in the IoT not as a specific “thing”, but rather a “class” of things.
Attributes of the class - such as the same automobile brake part failing repeatedly - are able to convey understanding and awareness at an aggregate level. Intelligent software need only monitor the class to know when to recommend a safety recall.
This is no different than an IP-enabled heat pump informing a service center that a motor is in need of maintenance.
The IoT must embrace classes of things as readily as discrete things. Failure to do so rules out the benefits that can be derived from a large and compelling aspect of big data.
Discrete Recognition Events
Image recognition events that identify a specific thing instantly transform physical objects into unique members of the IoT. Such events are precise and able to capture time, place, and condition of a specific physical object. These objects are immutable or at least near-immutable in the context they exist.
Camera technology has become so inexpensive and connectivity so pervasive, that it's economically practical to assign cameras to specific physical objects to monitor performance, location, and condition. Combined with recognition software, the camera itself becomes an IoT proxy for the physical object and is thus capable of detecting change.
Image recognition technology has the capacity to swiftly embrace physical objects that were never designed to participate in the Internet of Things.
Despite the ability to include inert physical artifacts in the IoT, recognized objects lack the capacity to listen for and act upon digital instructions.
Certainly, computer-aided vision technology is good, but it can't magically will a motor to repair itself. In fact, most IP-enabled devices will require human intervention for many situations and for decades to come.
However, the proxy relationship established through recognition software has the capacity to compel other systems and people to take action upon these inert objects. The cost of repeated monitoring through image detection is extremely low, giving us the economic latitude to perform millions of observation events.
Indeed, recognition events create read-only instantiations within the IoT, but awareness about these events can be used to indirectly effectuate change to the objects.
The Internet of Everything
There's a massive wave of data coming to the IoT from images and video. Perhaps today's big data movement is prologue to a future when the Internet of Things is transformed into the Internet of Everything.
For this transformation to occur it will largely depend on image recognition technology that enables identity by visual proxy.