Smart Eye Technology | By Bhumi Arora

Smart eye technology is an eye tracking system which uses digital cameras to track the movement of eyes and is used in Driver Monitoring systems. In the Driver monitoring system, the driver’s alertness level is assessed and if needed, brakes are applied. In simple words, Smart eye's automotive solutions Business works on software development necessary for eye tracking technology in vehicle’s interior environment for safety purposes. The whole thing focuses on the fact that in the future this technology will help in making a shift towards fully autonomous cars which will seamlessly transfer control of the vehicle and will react to the driver’s intentions and mental state to avoid accidents. This technology also focuses on customizing the car’s interior systems and features according to individuals like car seats or windshield wipers too which get activated by the driver's gaze. Smart eye technology uses Artificial Intelligence to provide more reliable data about driver’s actions.

THE FUNCTIONING

Now, how does this technology work? The basic process of this technology involves providing data to systems for analyzing. Camera based monitoring system is used for capturing eye movements and facial features. Rays of infrared light are used by the cameras to detect a reflection in each eye’s cornea. Now, AI-supported algorithms interpret the details of the face captured – like the person’s state of mind or mood. Whenever a new face is recognized, the system understands the characteristics a little better resulting in better results. Before Moving on to the complex understanding of this technology, let’s talk about computer vision first. Computer vision is the umbrella term used for deriving meaningful information from an image or a video. So, smart eyes use computer vision. Now, there are two computer vision methods: traditional computer vision methods and deep learning methods.

TRADITIONAL COMPUTER VISION METHOD AND DEEP LEARNING METHOD

Smart eye technology combines traditional computer vision with modern deep learning. This is because life is unpredictable and we are using this technology to analyze human behaviors while driving. So, we need to make sure that the results are efficient and accurate. To use the best methods for the problem at hand, we cannot just throw away traditional methods. The approach is to apply both depending on the situation.

Traditional computer vision methods involve manual coding and they analyze the different components of an image individually. Deep learning methods involve analyzing the entire image as a unit when fed with many datasets. For example- The AI algorithm method is trained by feeding large amounts of recorded data of drivers looking both tired and alert. Deep learning methods are based on AI technology which consists of many-layered neural networks at its core.

NEURAL NETWORK

Now, the next important question arises. What exactly is a Neural Network? The concept of neural networks has its roots in artificial intelligence. There are several types of neural networks consisting of small blocks. When these small blocks are connected to each other, a neural network is formed just like a biological one mimicking the way the human brain operates. For the training of the neural network, the network is supplied with example images, training data, while simultaneously indicating to the network what the image portrays. In simple words, let us suppose that we are training the neural network to differentiate between two different types of animals. Now, we will feed the network an image of a cat. Also, we are going to let it know that it is a cat. This will help the network in identifying the features that are of a cat. Now, we will show the network with thousands of images that are different from each other in numerous ways but same in one way, i.e., It is an image of a cat. The motive to do so is to train the system with various details that help in identifying a cat. For the next step, we will show the network lots of images containing different animals with the information of the animal represented in it. When the neural network is fully trained, it needs no information regarding the images. Relating the above example with technology, what we do is feed the system with massive images of human faces, humans of different genders, ages, ethnicities. Just the same way, we must inform the system that these images have human faces with different expressions that one can have. So, that is what a neural system is and how it is trained as it is at the core of the deep learning methods used in smart eye technology with manual coding methods.

THE OUTPUT

The opinion is that when we use the traditional computer vision method with modern deep learning methods; both these methods use different approaches to reach conclusions through completely different processes. This allows for one method to be used to get a “second opinion” on the other’s output. If the output from both the methods is similar then, we can be sure that the result is correct. If the outputs are different, it would mean that the image needs to be further analyzed.

We cannot be careless with the results in the automotive industry. Having an incorrect output would mean risking the driver’s life. Thus, it is better to say that we are unsure about what the driver is doing or in which direction he is looking. So, that car reacts in a different way for the safety of the driver.

REQUIREMENTS

The automotive industry requires performance, precision and availability. Firstly, these solutions must work on an optimized platform, use processor power conservatively and are cost effective. Second, technology must help us in knowing where the person is looking at, how open the eyelids are with a high level of precision and thirdly, it must work in the whole world and in various situations whether the light conditions are good or not or if the driver is wearing shades or a mask.

THE SUPPLY CHAIN

In the automotive industry, Original Equipment Manufacturers (OEM) produce original equipment focusing on designing, promoting, assembling vehicles etc. Tier 1 suppliers are those that supply parts directly to OEMs. The purchase of components-based products such as smart eye’s eye tracking software is done by OEMs from Tier 1 suppliers who purchase it from tier 2 suppliers, i.e, Smart eye.

CONCLUSION

In my Opinion, collaborating with the automotive industry means that they need to have an exceptional standard of technology. As the industry depends on smart eye technology for safety, they have to be serious regarding the job. Also, the software should be readily available. We know that it is pretty expensive to install these complex computer systems in vehicles and they use a lot of resources for this but they need to develop adaptable systems that are cost efficient and use processor power conservatively. Also, I believe Smart eye technology has to be updated regularly with the fast-paced development of the automotive industry and innovative solutions should be searched for solving the present and future problems. The future is uncertain, Thus, there is a hope of advancement in technology. It is speculated that the future will bring technological innovations that could make way for a new generation of AI-technology which will not be completely based on deep learning technology, a technology which could handle more information, more history and could make more critical decisions in the future.

Smart Eye technology is not just another tech company, it creates a technology that is the leading interface between human and artificial intelligence. It focuses on not only breaking new grounds in terms of advanced, AI based technology but also on making a long-term difference by improving road safety all over the world. The belief is that the path towards safer roads will be paved by innovative technology. At the end of the day, There is a hope that a right algorithm will mean that another life is saved, making all the efforts worthwhile.