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001 90986
003 ft7901
005 20251119145042.0
008 240830n 000 0 eng d
040 _erda
041 _aengtag
050 _aTK7800 H47 2024
082 _a.
100 _aHernandez, Jaira S.; Lenio, Neña Mae S.; Manalili, Ian Exequiel S.; Pardales, Fernando Jr. T.
245 0 _aReal-time driver drowsiness and distraction detection using convolutional neural network with multiple behavioral features
264 _a.
_b.
_cc2024
300 _bUndergraduate Thesis: (Bachelor of Science in Electronics Engineering) - Pamantasan ng Lungsod ng Maynila, 2024.
336 _b.
_atext
_2rdacontent
337 _30
_b.
_aunmediated
_2rdamedia
338 _30
_b.
_avolume
_2rdacarrier
505 _aABSTRACT: Road accidents caused by driver drowsiness and distraction represent significant threats to worldwide road safety, with facilities and injuries at alarming rates in the Philippines. With asignificant amount of casualties, the need for proactive measures is urgent. Recognizing the human factor as the primary cause of accidents, this study aimed to develop a real-time driver drowsiness and distraction detection system to mitigate risks. Using non-intrusive camera sensors and convolutional neural networks (CNN), the system monitors the driver behaviour, including facial expressions, eye movements, and lane position, to detect signs of drowsiness and distraction. This study meticulously outlines the systematic procedures, employing a quantitative developmental research approach to design and assess the effectiveness of the system. Real-world on-road testing with participants engaged in long-duration driving ensures the authenticity of data collection. The findings highlight the sytem’s prmising performance in drowsiness and distraction detection, with high accuracy rates and effective alert system triggered upon detection of potential risks. The integration of CNN technology underscores the system’s potential to significantly enhance road safety authorities. This research sets a foundation for future advancements in proactive driver safety technologies, emphasizing the critical importance of addressing driver drowsiness and distraction on the roads.
520 _aABSTRACT:
521 _aRoad accidents caused by driver drowsiness and distraction represent significant threats to worldwide road safety, with facilities and injuries at alarming rates in the Philippines. With asignificant amount of casualties, the need for proactive measures is urgent. Recognizing the human factor as the primary cause of accidents, this study aimed to develop a real-time driver drowsiness and distraction detection system to mitigate risks. Using non-intrusive camera sensors and convolutional neural networks (CNN), the system monitors the driver behaviour, including facial expressions, eye movements, and lane position, to detect signs of drowsiness and distraction. This study meticulously outlines the systematic procedures, employing a quantitative developmental research approach to design and assess the effectiveness of the system. Real-world on-road testing with participants engaged in long-duration driving ensures the authenticity of data collection. The findings highlight the sytem's prmising performance in drowsiness and distraction detection, with high accuracy rates and effective alert system triggered upon detection of potential risks. The integration of CNN technology underscores the system's potential to significantly enhance road safety authorities. This research sets a foundation for future advancements in proactive driver safety technologies, emphasizing the critical importance of addressing driver drowsiness and distraction on the roads.
526 _aF
655 _aacademic writing
753 _c6
942 _2lcc
_cARCHIVES
999 _c25807
_d25807