This study presents a novel driver drowsiness detection system that combines
deep learning techniques with the OpenCV framework. The system utilises facial
landmarks extracted from the driver's face as input to Convolutional Neural
Networks trained to recognise drowsiness patterns. The integration of OpenCV
enables real-time video processing, making the system suitable for practical
implementation. Extensive experiments on a diverse dataset demonstrate high
accuracy, sensitivity, and specificity in detecting drowsiness. The proposed
system has the potential to enhance road safety by providing timely alerts to
prevent accidents caused by driver fatigue. This research contributes to
advancing real-time driver monitoring systems and has implications for
automotive safety and intelligent transportation systems. The successful
application of deep learning techniques in this context opens up new avenues
for future research in driver monitoring and vehicle safety. The implementation
code for the paper is available at
this https URL

本研究提出了一种新的司机瞌睡检测系统，结合了深度学习技术和 OpenCV 框架，利用从司机脸部提取的面部标志作为卷积神经网络的输入，训练模型识别瞌睡模式，并集成 OpenCV 实现实时视频处理，从而使该系统适用于实际应用。广泛的实验证明该系统在检测瞌睡方面具有高准确性、敏感性和特异性，能够通过及时提醒来防止由司机疲劳引起的事故，这对提高道路安全具有潜在的意义。该研究对于推进实时司机监控系统以及汽车安全和智能交通系统具有重要意义，而在这一背景下成功应用深度学习技术为未来司机监控和车辆安全的研究开辟了新的方向。

VigilEye：基于人工智能的实时驾驶员疲劳检测

VigilEye -- Artificial Intelligence-based Real-time Driver Drowsiness  Detection

The recognition of human actions in videos is one of the most active research
fields in computer vision. The canonical approach consists in a more or less
complex preprocessing stages of the raw video data, followed by a relatively
simple classification algorithm. Here we address recognition of human actions
using the reservoir computing algorithm, which allows us to focus on the
classifier stage. We introduce a new training method for the reservoir
computer, based on "Timesteps Of Interest", which combines in a simple way
short and long time scales. We study the performance of this algorithm using
both numerical simulations and a photonic implementation based on a single
non-linear node and a delay line on the well known KTH dataset. We solve the
task with high accuracy and speed, to the point of allowing for processing
multiple video streams in real time. The present work is thus an important step
towards developing efficient dedicated hardware for video processing.

本文提出了基于 “兴趣时间步” 的水库计算算法的新训练方法，研究了其在人类行为识别中的表现，同时还在 KTH 数据集上使用单个非线性节点和时延线进行了光子实现。该算法表现出高准确性和速度，能够实现多个视频流的实时处理。这是开发高效专用硬件进行视频处理的重要一步。

利用光子储备计算机进行高速人体动作识别

High Speed Human Action Recognition using a Photonic Reservoir Computer

We propose an effective online background subtraction method, which can be
robustly applied to practical videos that have variations in both foreground
and background. Different from previous methods which often model the
foreground as Gaussian or Laplacian distributions, we model the foreground for
each frame with a specific mixture of Gaussians (MoG) distribution, which is
updated online frame by frame. Particularly, our MoG model in each frame is
regularized by the learned foreground/background knowledge in previous frames.
This makes our online MoG model highly robust, stable and adaptive to practical
foreground and background variations. The proposed model can be formulated as a
concise probabilistic MAP model, which can be readily solved by EM algorithm.
We further embed an affine transformation operator into the proposed model,
which can be automatically adjusted to fit a wide range of video background
transformations and make the method more robust to camera movements. With using
the sub-sampling technique, the proposed method can be accelerated to execute
more than 250 frames per second on average, meeting the requirement of
real-time background subtraction for practical video processing tasks. The
superiority of the proposed method is substantiated by extensive experiments
implemented on synthetic and real videos, as compared with state-of-the-art
online and offline background subtraction methods.

本文提出了一种基于高斯混合模型 (MoG) 的在线背景消除方法，该方法使用了一种特殊的 MoG 混合模型对每一帧的前景进行建模，并通过前一帧的前景 / 背景知识对 MoG 混合模型进行规范化。此外，本文还嵌入了仿射变换操作符来适应各种视频背景变换，并通过子采样技术来实现实时视频处理。与现有的在线和离线背景消除方法相比，实验表明，所提出的方法具有优越性能。