Drowsiness Detection and Accident Avoidance System in Vehicles
| Main Authors: | Pawan Bhardwaj, Anush CN, TL Shashwath Patel, Arun kumar M, Karthik BR |
|---|---|
| Format: | Article eJournal |
| Bahasa: | eng |
| Terbitan: |
, 2019
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/2702959 |
| ctrlnum |
2702959 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Pawan Bhardwaj</creator><creator>Anush CN</creator><creator>TL Shashwath Patel</creator><creator>Arun kumar M</creator><creator>Karthik BR</creator><date>2019-05-10</date><description>Driver drowsiness is one of the major causes of road accidents and it can lead to serious physical injuries, loss of human life, damage to property & loss of money. So a reliable driver drowsiness detection system is needed to be implemented, which would alert the driver before anything undesired happens. In this paper, design and implementation of 'Driver Drowsiness Detection System with Audio-Visual Warning' will be discussed. This system is to be evolved for car driver, but the scope of this system is far more than it. It can be used in any situation where a person's drowsiness is needed to be monitored. The planned system can use a camera that takes pictures of driver's face and monitors the driver's eyes in order to detect drowsiness of driver.
Once fatigue is detected, the alarm will be used to alert the driver. The proposed system will work in 3 main stages, in 1st stage the face of the driver is detected and tracked. In the 2nd stage the facial features are extracted for further processing. In last stage, eye's status is monitored. In 3rd stage it is determined that whether the eyes are closed or open. On the basis of this result the warning is issued to the driver. For this Raspberry pi with raspbian (Linux) OS is used. The camera will be connected through USB port of Raspberry pi. The picture processing will be done using OpenCV.</description><identifier>https://zenodo.org/record/2702959</identifier><identifier>10.5281/zenodo.2702959</identifier><identifier>oai:zenodo.org:2702959</identifier><language>eng</language><relation>doi:10.5281/zenodo.2702958</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><source>IJISE 1(2) 79-87</source><subject>Advanced Vehicle Safety, Driver Drowsiness Detection, Driver Fatigue, Raspberry-pi, Raspbian, VehicleAccident Warning</subject><title>Drowsiness Detection and Accident Avoidance System in Vehicles</title><type>Journal:Article</type><type>Journal:Article</type><recordID>2702959</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Journal:eJournal |
| author |
Pawan Bhardwaj Anush CN TL Shashwath Patel Arun kumar M Karthik BR |
| title |
Drowsiness Detection and Accident Avoidance System in Vehicles |
| publishDate |
2019 |
| topic |
Advanced Vehicle Safety Driver Drowsiness Detection Driver Fatigue Raspberry-pi Raspbian VehicleAccident Warning |
| url |
https://zenodo.org/record/2702959 |
| contents |
Driver drowsiness is one of the major causes of road accidents and it can lead to serious physical injuries, loss of human life, damage to property & loss of money. So a reliable driver drowsiness detection system is needed to be implemented, which would alert the driver before anything undesired happens. In this paper, design and implementation of 'Driver Drowsiness Detection System with Audio-Visual Warning' will be discussed. This system is to be evolved for car driver, but the scope of this system is far more than it. It can be used in any situation where a person's drowsiness is needed to be monitored. The planned system can use a camera that takes pictures of driver's face and monitors the driver's eyes in order to detect drowsiness of driver.
Once fatigue is detected, the alarm will be used to alert the driver. The proposed system will work in 3 main stages, in 1st stage the face of the driver is detected and tracked. In the 2nd stage the facial features are extracted for further processing. In last stage, eye's status is monitored. In 3rd stage it is determined that whether the eyes are closed or open. On the basis of this result the warning is issued to the driver. For this Raspberry pi with raspbian (Linux) OS is used. The camera will be connected through USB port of Raspberry pi. The picture processing will be done using OpenCV. |
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Universitas PGRI Palembang |
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Perpustakaan Universitas PGRI Palembang |
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Marga Life in South Sumatra in the Past: Puyang Concept Sacrificed and Demythosized |
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SUMATERA SELATAN |
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