Monitoring System of Drowsiness and Lost Focused Driver Using Raspberry Pi

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ADI Kusworo; WIDODO Catur Edi; WIDODO Aris Puji; Nurul ARISTIA Hilda

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Journal Paper

Paper Information

Journal: Iranian Journal of Public Health Year:2020 | Volume:49 | Issue:9 Page(s): 1675-1682

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Information Journal Paper

Title

Monitoring System of Drowsiness and Lost Focused Driver Using Raspberry Pi

Author(s)

ADI Kusworo | WIDODO Catur Edi | WIDODO Aris Puji | Nurul ARISTIA Hilda | Issue Writer Certificate

Keywords

Lost focused driver

Drowsiness detection

Haar cascade classifier

Real-time

Raspberry Pi

Abstract

Background: Drowsiness condition is one of the significant factors often encountered when an accident occurs. We aimed to detect a method to prevent accidents caused by drowsiness and lost a focused driver. Methods: The image processing technique has been capable of detecting the characteristic of drowsiness and lost focus driver in Real-time using Raspberry Pi. Video samples were processed using the Haar cascade classifier method to identify areas of the face, eyes, and mouth so that drowsy conditions. The methods can be determined based on the bject detected. Results: Two parameters were determined, the lost focused and drowsiness driver. The highest accuracy value for driver lost focused detection was 88. 00%, while the highest accuracy value for drowsiness driver detection was 90. 40%. Conclusion: In general, a system developed with image processing methods has been able to monitor the drowsiness and Lost focused drivers with high accuracy. This system still needs improvements to increase per-formance.

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APA: Copy

ADI, Kusworo, WIDODO, Catur Edi, WIDODO, Aris Puji, & Nurul ARISTIA, Hilda. (2020). Monitoring System of Drowsiness and Lost Focused Driver Using Raspberry Pi. IRANIAN JOURNAL OF PUBLIC HEALTH, 49(9), 1675-1682. SID. https://sid.ir/paper/750149/en

Vancouver: Copy

ADI Kusworo, WIDODO Catur Edi, WIDODO Aris Puji, Nurul ARISTIA Hilda. Monitoring System of Drowsiness and Lost Focused Driver Using Raspberry Pi. IRANIAN JOURNAL OF PUBLIC HEALTH[Internet]. 2020;49(9):1675-1682. Available from: https://sid.ir/paper/750149/en

IEEE: Copy

Kusworo ADI, Catur Edi WIDODO, Aris Puji WIDODO, and Hilda Nurul ARISTIA, “Monitoring System of Drowsiness and Lost Focused Driver Using Raspberry Pi,” IRANIAN JOURNAL OF PUBLIC HEALTH, vol. 49, no. 9, pp. 1675–1682, 2020, [Online]. Available: https://sid.ir/paper/750149/en

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