Driver Distraction and Drowsiness Detection System using Python, AI, and Computer Vision
AI-Powered Driver Monitoring System: Distraction and Drowsiness Detection using Python & 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..
Course Content
- Introduction of the Driver Distraction System –> 1 lecture • 1min.
- Environment Setup for Python Development –> 2 lectures • 3min.
- Driver Distraction Project Overview –> 1 lecture • 1min.
- Google Colab Setup & Google Drive Mount –> 1 lecture • 2min.
- Dataset Download & Exploration –> 1 lecture • 2min.
- Data Visualization & Insights –> 1 lecture • 3min.
- Data Preprocessing & Augmentation –> 1 lecture • 12min.
- ResNet-50 Model Architecture & Implementation –> 1 lecture • 16min.
- Model Training & Optimization –> 1 lecture • 12min.
- Model Inference Code Explanation –> 1 lecture • 6min.
- Code Execution –> 1 lecture • 5min.
- Introduction of the Driver Drowsiness Detection System –> 1 lecture • 1min.
- Environment Setup for Python Development –> 2 lectures • 3min.
- Driver Drowsiness Detection Project Overview –> 1 lecture • 1min.
- Understanding Key Packages for Driver Drowsiness Detection –> 1 lecture • 1min.
- Implementing Drowsiness Detection Logic Using EAR and MAR –> 1 lecture • 2min.
- Integrating Drowsiness Detection with Tkinter GUI –> 1 lecture • 1min.
- Real-Time Driver Drowsiness Detection with Live Video Streaming –> 1 lecture • 2min.
- Real-Time Model Inference for Driver Drowsiness Detection –> 1 lecture • 2min.
- Wrapping Up –> 1 lecture • 1min.
Requirements
AI-Powered Driver Monitoring System: Distraction and Drowsiness Detection using Python & Computer Vision
Welcome to this all-in-one, hands-on course where you’ll learn to develop an intelligent AI-powered system capable of detecting driver distractions and drowsiness in real-time using Python, Computer Vision, and Deep Learning.
This course combines the power of ResNet50 for distraction detection and facial landmark-based algorithms for drowsiness detection, offering a complete solution for road safety and driver monitoring.
What You’ll Learn:
Distraction Detection Module:
- Use the State Farm Driver Distraction dataset to train a model that identifies 10 different distraction activities such as texting, eating, adjusting the radio, or talking to passengers.
- Train a ResNet50 deep learning model using TensorFlow/Keras.
- Apply data preprocessing, augmentation, transfer learning, and hyperparameter tuning to improve model accuracy.
- Build a real-time distraction detection system using OpenCV and integrate it with a Tkinter-based GUI and web interface.
- Deploy your model for use in real-world scenarios like fleet management and AI safety systems.
Drowsiness Detection Module:
- Capture and process real-time video feeds using Python and OpenCV.
- Extract facial landmarks using MediaPipe to analyze eye and mouth movements.
- Calculate Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) to detect signs of fatigue, yawning, and drowsiness.
- Implement logic to trigger real-time alerts and visual warnings when drowsiness is detected.
- Create a Tkinter-based UI to display status and metrics in real-time.
By the end of this course, you will:
- Build a dual-function Driver Monitoring System that detects both distractions and drowsiness.
- Gain practical, hands-on experience in AI, computer vision, deep learning, and GUI development.
- Be equipped to deploy your project in real-world applications across transportation, logistics, and safety systems.
Whether you’re a beginner or an intermediate Python developer, this course is designed to provide valuable, real-world experience in building AI-powered safety solutions.