AI Driver Distraction & Drowsiness Detection with Python&CV

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.

Description
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 anintelligent AI-powered systemcapable of detectingdriver distractions and drowsiness in real-timeusingPython, Computer Vision, and Deep Learning.

This course combines the power ofResNet50 for distraction detectionandfacial landmark-based algorithms for drowsiness detection, offering a complete solution forroad safetyanddriver monitoring.

What You’ll Learn:

Distraction Detection Module:

Use theState Farm Driver Distraction datasetto train a model that identifies 10 different distraction activities such as texting, eating, adjusting the radio, or talking to passengers.

Train aResNet50 deep learning modelusing TensorFlow/Keras.

Apply data preprocessing, augmentation, transfer learning, and hyperparameter tuning to improve model accuracy.

Build areal-time distraction detection systemusing OpenCV and integrate it with aTkinter-based GUIand web interface.

Deploy your model for use in real-world scenarios likefleet managementandAI safety systems.

Drowsiness Detection Module:

Capture and processreal-time video feedsusing Python and OpenCV.

Extractfacial landmarksusingMediaPipeto analyze eye and mouth movements.

CalculateEye Aspect Ratio (EAR)andMouth Aspect Ratio (MAR)to detect signs of fatigue, yawning, and drowsiness.

Implement logic to triggerreal-time alerts and visual warningswhen drowsiness is detected.

Create aTkinter-based UIto display status and metrics in real-time.

By the end of this course, you will:

Build a dual-functionDriver Monitoring Systemthat detectsboth distractions and drowsiness.

Gain practical, hands-on experience inAI, computer vision, deep learning, and GUI development.

Be equipped to deploy your project in real-world applications acrosstransportation, 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.

Who this course is for:
- Students eager to learn AI through hands-on projects in drowsiness detection and driver behavior analysis for road safety and monitoring.
- Professionals wanting to upskill in AI, ML, and Python for real-world use, including driver assistance and safety systems in transportation.
- IoT enthusiasts looking to combine AI with IoT solutions for intelligent driver monitoring and real-time safety alerts in connected vehicles.
- Aspiring developers and researchers aiming to build careers or AI-based solutions for accident prevention and smart mobility systems.
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