In view of the fact that the detection of driver's distraction is a burning issue, this study chooses the driver's head pose as the evaluation parameter for driving distraction and proposes a driver distraction method based on the head pose. The effects of single regression and classification combined with regression are compared in terms of accuracy, and four kinds of classical networks are improved and trained using 300W-LP and AFLW datasets. The HPE_Resnet50 with the best accuracy is selected as the head pose estimator and applied to the ten-category distracted driving dataset SF3D to obtain 20,000 sets of head pose data. The differences between classes are discussed qualitatively and quantitatively. The analysis of variance shows that there is a statistically significant difference in head posture between safe driving and all kinds of distracted driving at 95% and 90% confidence levels, and the postures of all kinds of driving movements are distributed in a specific Eul er angle range, which provides a characteristic basis for the design of subsequent recognition methods. In addition, according to the continuity of human movement, this paper also selects 90 drivers' videos to analyze the difference in head pose between safe driving and distracted driving frame by frame. By calculating the spatial distance and sample statistics, the results provide the reference point, spatial range, and threshold of safe driving under this driving condition. Experimental results show that the average error of HPE_Resnet50 in AFLW2000 is 6.17° and that there is an average difference of 12.4° to 54.9° in the Euler angle between safe driving and nine kinds of distracted driving on SF3D.
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