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Build a system to monitor and detect distracted driving behavior such as mobile usage or looking away. Use deep learning models to improve road safety.
Intermediate-level. Certificate included.
Startups
Microsoft
NVIDIA
By the end of this course, you'll have the skills to land your first computer vision job or freelance clients.
Understand how AI monitors driver behavior to detect signs of distraction or inattention.
Use Python with OpenCV and deep learning models to identify distracted driver activities like phone use or drowsiness.
Build a real-time system that tracks driver facial expressions and head movements using a webcam.
Record distraction events with timestamps in a database for alert generation and reporting.
Create an interactive GUI to display alerts, live feed, and distraction status in real time.
Finish the course and receive a verified certificate of success.
Muhammad Yaqoob is the founder of Tentosoft Pvt Ltd and a seasoned Computer Vision expert. With 10+ years of experience and over 5,000+ students taught globally, he brings deep industry knowledge and a passion for practical, hands-on learning.
8 weeks of comprehensive training with 50+ lessons and 10+ hours of content
Driver Distraction System is a deep learning-based approach that identifies and classifies various forms of driver distractions using visual inputs. This system aims to enhance road safety by monitoring driver behavior in real-time through a camera feed.
Course Introduction and Features
Detect distracted drivers
Use deep learning models
Identify risky actions
The Python development environment is a collection of tools and libraries required to build, train, and deploy AI models. It includes installing Python, setting up IDEs, managing dependencies using pip, and creating isolated environments with virtualenv.
Installing Python
VS Code Setup for Python Development
The Driver Distraction Project is a computer vision task designed to detect driver inattention from image data. It involves data collection, preprocessing, training CNN models, and deploying them for real-time classification of distraction categories.
Driver Distraction System Project Overview
Detect driver inattention
Train CNN models
End-to-end CV project
Google Colab is a cloud-based Jupyter notebook that provides free GPU access for machine learning tasks. Mounting Google Drive allows access to datasets and models stored in the cloud, ensuring seamless integration with the project workflow.
Google Colab Setup & Google Drive Mount
Use Colab online
Access cloud datasets
No local setup needed
The dataset for driver distraction includes categorized images of drivers performing different actions. Dataset exploration involves understanding the class distribution, inspecting image quality, and preparing the data for training and validation.
Dataset Download & Exploration
Analyze data balance
Understand categories
Know your data well
Data visualization is the graphical representation of dataset statistics and model performance. It includes plots for class distribution, training accuracy, and confusion matrices, helping in understanding patterns and improving model decisions.
Data Visualization & Insights
Plot class distribution
Spot data imbalances
Data preprocessing involves cleaning and resizing images to standard dimensions, while augmentation increases dataset variability using techniques like flipping, rotation, and zoom. This ensures robustness and generalization of the model.
Data Preprocessing & Augmentation
Clean and resize images
Prepare for training
ResNet-50 is a deep convolutional neural network that solves the vanishing gradient problem using residual connections. It is widely used in image classification tasks and serves as the backbone for driver distraction detection models.
ResNet-50 Model Architecture & Implementation
Use deep CNN model
Fine-tune on dataset
Model training involves feeding input data into the neural network and optimizing weights using a loss function. Optimization techniques such as learning rate tuning, regularization, and dropout are applied to improve accuracy and prevent overfitting.
Model Training & Optimization
Tune learning rate
Improve model accuracy
Model inference is the process where a trained model predicts the class of new input images. This code section explains how images are passed through the network, outputs are decoded, and results are displayed in real-time or batch mode.
Model Inference Code Explanation
Pass through network
Display results clearly
Is This Course Right for You?
Kickstart your AI journey with structured, hands-on learning.
Build a portfolio that recuriters can't ignore.
Add powerful AI/CV features to your apps and software.
Upskill for higher-paying, future-ready tech roles.
Build Smarter, more intelligent applications.
Transition into AI even with zero background.
One-time payment for lifetime access to all course materials and updates
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Highly recommended for small teams who seek to upgrade their time & perform.
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