Video based detection of driver fatigue

Official URL: http://192.168.1.20/record=b1293775 (Table of Contents)

This thesis addresses the problem of drowsy driver detection using computer vision techniques applied to the human face. Specifically we explore the possibility of discriminating drowsy from alert video segments using facial expressions automatically extracted from video. Several approaches were previously proposed for the detection and prediction of drowsiness. There has recently been increasing interest in computer vision approaches as it is a potentially promising approach due to its non-invasive nature for detecting drowsiness. Previous studies with vision based approaches detect driver drowsiness primarily by making pre-assumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to explore, understand and exploit actual human behavior during drowsiness episodes. We have collected two datasets including facial and head movement measures. Head motion is collected through an accelerometer for the first dataset (UYAN-1) and an automatic video based head pose detector for the second dataset (UYAN-2). We use outputs of the automatic classifiers of the facial action coding system (FACS) for detecting drowsiness. These facial actions include blinking and yawn motions, as well as a number of other facial movements. These measures are passed to a learning-based classifier based on multinomial logistic regression. In UYAN-1 the system is able to predict sleep and crash episodes during a driving computer game with 0.98 performance area under the receiver operator characteristic curve for across subjects tests. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis reveals new information about human facial behavior during drowsy driving. In UYAN-2 fine discrimination of drowsy states are also explored on a separate dataset. The degree to which individual facial action units can predict the difference between moderately drowsy to acutely drowsy is studied. Signal processing techniques and machine learning methods are employed to build a person independent acute drowsiness detection system. Temporal dynamics are captured using a bank of temporal filters. Individual action unit predictive power is explored with an MLR based classifier. Best performing five action units have been determined for a person independent system. The system is able to obtain 0.96 performance of area under the receiver operator characteristic curve for a more challenging dataset with the combined features of the best performing 5 action units. Moreover the analysis reveals new markers for different levels of drowsiness.