Drowsiness is one of the most common causes of car accidents. It is estimated that 30% to 40% of all accidents are related to drivers falling asleep. Our system is able to detect driver’s drowsiness based on input from a basic camera, which can even be the one embedded in your mobile phone or navigation
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We demonstrate a system which estimates direction of gaze for users in a controlled environment (e.g. sitting in front of a screen). Computational costs are reduced to a minimum by using the same technology both for eye corner locator and eye center locator. If high mapping precision is not a priority of the application, the
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This system is analyzing driver’s awareness by tracking head pose information. It can allow studies of driver behavior and report dangerous outcomes such as longer distractions by events away from the windshield.
The ubiquitous application of eye tracking has so far not been possible without dedicated and expensive hardware, such as infrared high definition cameras. With this system, we perform very accurate eye center localization and tracking in existing images or with a simple webcam.
Head pose and eye location estimation are two closely related problems. We demonstrate the system designed to (1) enhance the accuracy of eye location estimations in low resolution videos, (2) extend the operating range of the eye locator and (3) improve the accuracy and re-initialization capabilities of the head pose tracker.
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