Internet of Things Applications

How will you interact with the Internet of Things in your smart home of the future? Perhaps by looking your connected air conditioning unit in the lens from the comfort of your sofa and fanning your face with your hand to tell it to crank up its cooling jets.



At least that’s the vision of Italian startup Cogisen, which is hoping to help drive a new generation of richer interface technology that will combine different forms of interaction, such as voice commands and gestures, all made less error prone and/or abstract by adding “eye contact” into the mix. (If no less creepy… Look into the machine and the machine looks into you, right?)

The startup has built an image processing platform, called Sencogi, which has a first focus on gaze-tracking — with plenty of potential being glimpsed by the team beyond that, whether it’s helping to power vision systems for autonomous vehicles by detecting pedestrians, or performing other specific object-tracking tasks for niche applications as industry needs demand.

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“Vehicles will never be 100 percent autonomous. They’ll be decreased degrees of autonomy. It’s easy for a car to decide to take away control from you — but it’s very hard for the car to decide when to give you back control. For that they need to understand your attention, so you need gaze tracking,” adds Rijnders, discussing one potential use-case in the automotive domain.

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Rijnders’ background is in aerospace engineering. He previously worked for Ferrari developing simulators for Formula 1, which is where he says the germ of the idea to approach the hard problem of image processing from another angle occurred to him.

“There you have to do very non-linear, transient, dynamic multi-physics modeling, so very, very complex modeling, and I realized what the next generation of algorithms would need to be able to do for engineering. And at a certain point I realized that there was a need in image processing for such algorithms,” he says, of his time at Ferrari.

“If you think about the infinity of light conditions and different types of faces and points of view relative to the camera and camera quality for following sub-pixel movement of the irises — very, very difficult image processing problem to solve… We can basically detect signal signatures in image processing which are far more sparse and far more difficult than what has been possible up to now in the state of the art of image processing.”

See more at: techcrunch.com