Detecting driver inattention by rough iconic classification
Giovanni Luca Masala, Enrico Grosso · 2013
The paper proposes an original method, derived from basic face recognition and classification research, which is a good candidate for an effective automotive application. The proposed approach exploits a single b/w camera, positioned in front of the driver, and a very efficient classification strategy, based on neural network classifiers. A peculiar feature of the work is the adoption of iconic data reduction, avoiding specific and time-consuming feature-based approaches. Though at an initial development stage, the method proved to be fast and robust compared to state of the art techniques; experimental results show real-time response and mean weighted accuracy near to 93%. The method requires a simple training procedure which can be certainly improved for real applications; moreover it can be easily integrated with techniques for automatic face-recognition of the driver.