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Driving while drowsy is a major cause behind road accidents, and exposes the driver to a much higher crash risk compared to driving while alert. Therefore, the use of assistive systems that monitor a driver's level of vigilance and alert the driver in case of drowsiness can be significant in the prevention of accidents. This paper introduces the design and implementation of a lightweight system, in terms of computational complexity, which runs on a computationally-limited embedded smart camera platform to measure drivers' drowsiness based on yawning. Our system uses a modified implementation of the Viola-Jones algorithm for face and mouth detection, as well as the back projection theory for measuring both the rate and the amount of changes in the mouth for yawning detection. Our system is built on the top of an actual smart camera embedded platform, called APEX™ from CogniVue Corp., which is easy and practical for installation inside a car. The system is optimized in a way that meets the real time requirements of the monitoring task while relying on the limited processing power of the embedded platform.
Abtahi et al. (Wed,) studied this question.