At NVIDIA's recent GPU Technology Conference, we sat down with the CEO of Eyeris Technologies, Modar (JR) Alaoui, to discuss giving automobiles the ability to interpret human emotions. Eyeris is bringing to market a feature that has not been present in the 130 year history of the automobile. That feature is ability of the car to monitor and register the emotional state of the driver. The product will be able to record the emotions as shown by the driver's face, age identification, gender identification, eye tracking, and gaze estimation of drivers and passengers. "Our goal is to put our software in the back of every camera in the world," said Alouai.
Did you know that passengers invariably show a fear reaction when the brakes are applied in a car? That is just one of the things facial monitoring company Eyeris learned when developing its Emovu Driver Monitoring System (DMS). Using a combination of cameras, graphic processing and deep learning, Emovu analyzes the passengers in a car, determining from facial movement which of seven emotions these passengers are feeling. Modar JR Alaoui, CEO of Eyeris, demonstrated the company's in-car technology during Nvidia's GTC developer conference, putting forth a few ideas of how monitoring the emotions of drivers can lead to safer driving. The company used deep learning to train its Emovu software to recognize facial expressions.
Cars are getting smarter - and while many focus on seeing the road ahead, they are also set to begin analyzing drivers and passengers. This week at CES, the international consumer electronics show in Las Vegas, a host of startup companies are showing off inward facing cameras that watch and analyze drivers, passengers and objects in cars. Carmakers say they will boost safety - but privacy campaigners warn they could be used to make money by analyzing every movement - even being able to track a passenger's gaze to see what ads they are looking at, and monitor the emotions of people through their facial expressions. Occupants, inside a car, are seen on a monitor using technology by Silicon Valley company Eyeris, which uses cameras and AI to track drivers and passengers for safety benefits, shown during an interview in San Jose, California, U.S., December 28, 2018. Carmakers could gather anonymized data and sell it.
The development of the most advanced driver assistance systems (ADAS) in the industry should be based on integrated and open platforms. A complete solution is required for development, simulation, prototyping, and implementation to enable smarter, more sophisticated ADAS, and to pave the way for the autonomous car. This article summarizes the current status of DNN-based deep learning architectures built on top of a supercomputer on wheels, which are integrated in platforms to drive the future of autonomous vehicles. Deep learning is the most popular approach to develop AI. It is a way to enable machines to recognize and understand the world they are intended to operate in.
From Google's language translation app to autonomous cars, machine learning has become a key ingredient in multiple areas of our lives--but what exactly is it? In the simplest sense, machine learning is a method of computer data analysis that learns from its own experience. Once a machine learning algorithm learns what specific patterns look like, it can apply the knowledge on a vast scale. For example, a fraud detection machine learning algorithm may miss a few false charges initially, but once it identifies the pattern, it can protect against millions of future attacks. Check out this week's episode of Tech-x-planations to learn more about the basics of machine learning.