View Independent Drowsy Behaviour and Tiredness Detection
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 9
Abstract
Automatic identification of driver fatigue, drowsiness and sleepiness has many applications such as vehicle safety, operator safety and accident prevention, automatic monitoring and detection systems. This research paper examines use of color and depth data for detection of drowsy behavior. The data was recorded using two cameras, one on front and one on side view along with a depth sensor. The facial expressions, hand motion and body posture was used to classify fatigue, tiredness, sleepiness, drowsiness and boredom using support vector machines (SVM). In this study the upper torso and parts above waist were tracked. The evaluation of actions was done in dim as well as natural lighting inside a closed settings similar to interiors of a car. The recognition accuracy was better when data from both views was combined. The recognition accuracy was less in low lit and dim lighting settings inside car.
Authors and Affiliations
Amol S Patwardhan
Keywords
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- EP ID EP22629
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