Udemy - AI Driver Distraction and Drowsiness Detection with Pytho...
- Category Other
- Type Tutorials
- Language English
- Total size 1.2 GB
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Infohash : 99A4D4DF7ACB9EE194368AD1586ADFC4243687C6
AI Driver Distraction & Drowsiness Detection with Python&CV
https://WebToolTip.com
Published 5/2025
Created by Muhammad Yaqoob G
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 22 Lectures ( 1h 17m ) | Size: 1.12 GB
Driver Distraction and Drowsiness Detection System using Python, AI, and Computer Vision
What you'll learn
Understand the importance of driver drowsiness detection and the impact of distractions on road safety, and how AI-powered systems help mitigate these risks.
Set up a Python development environment and install libraries like OpenCV and MediaPipe for computer vision and distraction detection tasks.
Capture real-time video from a webcam and explore the State Farm Driver Distraction dataset to analyze and classify unsafe driver behaviors.
Extract facial landmarks such as eyes and mouth, and apply ResNet50 to classify ten types of driver distractions with high precision and accuracy.
Calculate Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) to detect drowsiness, and use visualization to improve deep learning model accuracy.
Implement algorithms to detect fatigue like eye closure and yawning, and optimize model performance using transfer learning and fine-tuning.
Develop a Tkinter-based GUI for real-time drowsiness alerts and distraction detection using live camera feeds with clear visual indicators.
Build an interactive user interface and integrate a web-based dashboard to enhance system usability and remote monitoring capabilities.
Combine all components into a working driver monitoring system that addresses challenges like low-light, occlusions, and varying driver postures.
Troubleshoot real-world issues and deploy the system for practical use in fleet monitoring, AI safety assistance, and driver training programs.
Requirements
Basic understanding of Python programming (helpful but not mandatory).
A laptop or desktop computer with internet access[Windows OS with Minimum 4GB of RAM).
No prior knowledge of AI or Machine Learning is required—this course is beginner-friendly
Enthusiasm to learn and build practical projects using AI and IoT tools.
Files:
[ WebToolTip.com ] Udemy - AI Driver Distraction and Drowsiness Detection with Python and CV- Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 1 - Introduction of the Driver Distraction System
- 1 -Course Introduction and Features.mp4 (25.5 MB)
- 1 -Model Inference Code Explanation.mp4 (68.9 MB)
- 1 -Code Execution.mp4 (54.8 MB)
- 1 -Course Introduction and Features.mp4 (32.6 MB)
- 1 -Installing Python.mp4 (15.2 MB)
- 2 -VS Code Setup for Python Development.mp4 (19.2 MB)
- 1 -Driver Drowsiness Detection System Project Overview.mp4 (4.1 MB) Driver Drowsiness Detection
- driver_drowsiness_detection.py (8.5 KB)
- requirements.txt (0.0 KB)
- 1 -Understanding Key Packages for Driver Drowsiness Detection.mp4 (7.8 MB)
- 1 -Calculating EAR and MAR for Driver Drowsiness Detection.mp4 (19.9 MB)
- 1 -Building a Tkinter GUI for Real-Time Drowsiness Detection.mp4 (9.8 MB)
- 1 -Implementing Real-Time Drowsiness Detection with Live Video Streaming.mp4 (27.7 MB)
- 1 -Implementing Model Inference for Drowsiness Detection.mp4 (20.6 MB)
- 1 -Installing Python.mp4 (15.2 MB)
- 2 -VS Code Setup for Python Development.mp4 (19.2 MB)
- 1 -Course Wrap-Up.mp4 (9.1 MB)
- 1 -Driver Distraction System Project Overview.mp4 (9.1 MB) Driver Distraction Monitoring System
- Driver_Distraction_Monitoring_System.ipynb (4.1 MB)
- Input Video 1.mp4 (373.7 KB)
- Input Video 2.mp4 (605.6 KB)
- inference.py (4.6 KB)
- requirements.txt (0.0 KB)
- resnet_50.py (8.6 KB)
- restnet_50.weights.h5 (320.2 MB)
- 1 -Google Colab Setup & Google Drive Mount.mp4 (20.0 MB)
- 1 -Dataset Download & Exploration.mp4 (17.8 MB)
- 1 -Data Visualization & Insights.mp4 (28.1 MB)
- 1 -Data Preprocessing & Augmentation.mp4 (124.7 MB)
- 1 -ResNet-50 Model Architecture & Implementation.mp4 (175.6 MB)
- 1 -Model Training & Optimization.mp4 (143.1 MB)
- Bonus Resources.txt (0.1 KB)
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