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  • Theses

基於人臉生物識別技術的駕駛員疲勞檢測研究

Study on Driver Fatigue Detection Based on Face Biometrics

Advisor : 楊淳良

Abstracts


本研究採用樹莓派4 B型開發板搭配攝像機,並採用了人臉部辨識與影像分析的技術來實現實時圖像識別,判定駕駛者是否處於疲勞或是分心狀態,以此降低發生交通意外事故。 在我們提出的系統中檢測到三個面向,所有這些面向都會向駕駛員發出警告聲,並通過LINE Notify將提醒消息發送給所有相關的智能手機。第一個面向是抓取臉部特徵其眼睛的部分來判定眼睛的開閉合度來檢測駕駛員的眼睛是否長時間閉合。第二個面向是分析嘴巴開閉合度來判定駕駛是否有打哈欠且計算其次數,以判斷駕駛員的哈欠次數是否超過預設值。第三個面向是評估駕駛員的注意力,即監視駕駛員是否在預設秒數內沒有向前看。 結合在同一即時圖像識別中檢測到的這三個面向,如果駕駛員的狀態符合條件之一,則提出的系統將提醒駕駛員他現在應該休息。

Parallel abstracts


This study utilized the Raspberry Pi 4 Model B development board with a camera and adopted face recognition and image analysis technologies to implement a real-time image recognition for determining whether the driver is fatigued or distracted, to reduce traffic accidents. There are three aspects detected in our proposed system, which all will alert a warning sound to the driver and send a reminder message to all concerned smartphones through LINE Notify. The first is to detect whether the driver’s eyes are closed for a long time by capturing the facial features of the eyes to determine the opening and closing of the eyes. The second is to judge whether the driver’s yawn times over the default by analyzing the opening and closing degree of the mouth for determining whether the driver has yawned and the count times. The third is to evaluate the driver’s attention, i.e., monitoring whether the driver does not look ahead over default seconds. Combining these three aspects detected in the same real-time image recognition, if the driver’s state meets one of the conditions, the proposed system will remind the driver that he should be resting now.

References


[1]Z. Mao and X.-M. Chu, “Advances of fatigue detecting technology for drivers,” China Safety Science Journal, vol. 15, no. 3, pp. 108-112, 2005.
[2] 蘇昭銘(2003)中華民國運輸學會-交通評論,“漫談駕駛疲勞,” available link: https://car.995.tw/ ?s=蘇昭銘
[3] 內政部警政署國道公路警察局全球資訊網,“交通事故統計分析,” available link: https://www.hpb.gov.tw/p/412-1000-98.php
[4] P. Smith, M. Shah and N. da Vitoria Lobo, “Determining driver visual attention with one camera,” IEEE Transactions on Intelligent Transportation Systems, vol. 4, no. 4, pp. 205-218, Dec. 2003.
[5] S. G. Klauer, T. A. Dingus, V. L. Neale, J. D. Sudweeks and D. J. Ramsey, “The impact of driver inattention on near-crash/crash risk: An analysis using the 100-car naturalistic driving study data,” U.S. Department of Transportation, National Highway Traffic Safety Administration, 2006.

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