| 000 -LEADER |
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| fixed length control field |
03929nam a2200313Ia 4500 |
| 001 - CONTROL NUMBER |
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| control field |
90986 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
ft7901 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251119145042.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
240830n 000 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Description conventions |
rda |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
TK7800 H47 2024 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Hernandez, Jaira S.; Lenio, Neña Mae S.; Manalili, Ian Exequiel S.; Pardales, Fernando Jr. T. |
| 245 #0 - TITLE STATEMENT |
|---|
| Title |
Real-time driver drowsiness and distraction detection using convolutional neural network with multiple behavioral features |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2024 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Other physical details |
Undergraduate Thesis: (Bachelor of Science in Electronics Engineering) - Pamantasan ng Lungsod ng Maynila, 2024. |
| 336 ## - CONTENT TYPE |
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| Content type code |
. |
| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Materials specified |
0 |
| Media type code |
. |
| Media type term |
unmediated |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Materials specified |
0 |
| Carrier type code |
. |
| Carrier type term |
volume |
| Source |
rdacarrier |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
ABSTRACT: 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 ## - SUMMARY, ETC. |
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| Summary, etc. |
ABSTRACT: |
| 521 ## - TARGET AUDIENCE NOTE |
|---|
| Target audience note |
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. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| Classification |
Filipiniana |
| 655 ## - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
academic writing |
| 753 ## - SYSTEM DETAILS ACCESS TO COMPUTER FILES |
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| Operating system |
6 |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Source of classification or shelving scheme |
|
| Item type |
Archival materials |