{'fraction_non_alphanumeric': 0.020166073546856466, 'fraction_numerical': 0.021352313167259787, 'mean_word_length': 5.071942446043166, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Distracted Driver Behaviour Recognition with Computer Vision using Deep Convolutional Neural Network in Real World Application

Distraction of drivers while driving on roadways results in the death of 1.2 million people, approximately every year around the globe. Even though there are several improvements made in road and vehicle design, the total number of accidents is higher. In 2015, 3,477 people were dead and 3,91,000 were injured during motor vehicle crashes associating distracted drivers. Our paper is aimed to prevent and reduce the rate of motor vehicle accidents that are caused by human errors and distraction during driving. We study the different postures of the driver by means of the hand localization, skin segmentation and facial data. In our paper, we propose a reliable deep-learning CNN model that attains 92% accuracy.