Yawn Based Driver Fatigue Level Prediction

Kassem, Haider A.; Chowdhury, Mihir; Abawajy, Jemal; Al-Sudani, Ahmed Raad

doi:10.29007/67kk

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…However, because they ignore the temporal features of the drowsiness behaviors, these systems are hard to be considered reliable driver fatigue detection systems for real-life scenarios. Among the efforts on image-based driver drowsiness detection, the work of Kassem et al [13] proposed a yawning-based fatigue detection system that aggregated frame-level CNN outputs into three drowsiness levels based on the number of yawns per minute: alert, early fatigue, and fatigue. By classifying each frame as yawning or not.…”

Section: Image-based Systemsmentioning

confidence: 99%

“…The different style of drowsiness actions for each driver is the reason behind the complexity of DDD systems. For that, building a system that depends on only yawning detection [13,[19][20][21]23] or eye state recognition [15][16][17]22] cannot be used as an effective real-life DDD system.…”

Section: Nthu-dddmentioning

confidence: 99%

“…Some studies [13][14][15][16][17][18] did not apply sequence learning by depending only on the information of each image frame separately and ignoring the temporal features encoded between adjacent frames. For that, it will be hard to distinguish between yawning and a wide-open mouth for talking and singing.…”

Section: Without Any Sequence Learning For the Drowsiness Actionsmentioning

confidence: 99%

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A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM

Alameen¹,

Alhothali²

2023

Computer Systems Science and Engineering

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Today, fatalities, physical injuries, and significant economic losses occur due to car accidents. Among the leading causes of car accidents is drowsiness behind the wheel, which can affect any driver. Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents. This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos. This model depends on integrating a 3D convolutional neural network (3D-CNN) and long short-term memory (LSTM). The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features within adjacent frames. The learned features are then used as the input of the LSTM component for modeling high-level temporal features. In addition, we investigate how the training of the proposed model can be affected by changing the position of the batch normalization (BN) layers in the 3D-CNN units. The BN layer is examined in two different placement settings: before the non-linear activation function and after the non-linear activation function. The study was conducted on two publicly available drowsy drivers datasets named 3MDAD and YawDD. 3MDAD is mainly composed of two synchronized datasets recorded from the frontal and side views of the drivers. We show that the position of the BN layers increases the convergence speed and reduces overfitting on one dataset but not the other. As a result, the model achieves a test detection accuracy of 96%, 93%, and 90% on YawDD, Side-3MDAD, and Front-3MDAD, respectively.

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Section: Image-based Systemsmentioning

confidence: 99%