Our Instructors Collaborate With Top Tech Leaders
Startups
Microsoft
NVIDIA
Deploy the solution on live camera feeds to automatically detect
accidents, analyze scenes, and trigger instant alerts for quick
emergency response.
Beginner-friendly. Certificate included.
Startups
Microsoft
NVIDIA
By the end of this course, you'll have the skills to build AI-powered accident detection systems and secure computer vision jobs or freelance projects.
Learn how AI and computer vision detect accidents in real time.
Use Python, OpenCV, and deep learning frameworks for accident detection.
Build a system to detect accidents from live video feeds.
Implement real-time alert systems for accident notifications.
Design a user-friendly GUI to monitor and manage detection systems.
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
AI Detection and Real-Time Monitoring Systems leverage advanced algorithms to identify and track events in real-time, enabling rapid response in critical scenarios like accident detection.
Explores the role of AI in automating detection and monitoring processes
Highlights applications in safety, security, and traffic management systems
Introduces key concepts of computer vision and real-time data processing
Outlines the course structure and objectives for practical implementation
Setting up a Python development environment involves installing essential tools and configuring an IDE for efficient coding and debugging.
Install the latest Python version to ensure compatibility with modern libraries
Configure Visual Studio Code with Python extensions for streamlined development
Set up a virtual environment to manage project dependencies
Verify installation with a simple Python script execution
This system uses AI to detect accidents in real-time using video feeds, enabling quick alerts and responses for emergency situations.
Integrates computer vision models to analyze video streams for accident detection
Employs real-time data processing for immediate notifications and monitoring
Discusses system architecture for scalable deployment in real-world scenarios
Explores challenges like varying lighting conditions and diverse accident types
Google Colab provides a cloud-based Python environment with pre-installed libraries, ideal for machine learning projects with seamless Google Drive integration.
Open Google Colab and create a new notebook for project development
Mount Google Drive to access and store datasets and model files
Install additional libraries specific to the project requirements
Test the environment with a sample code execution
Essential Python packages like TensorFlow, OpenCV, and NumPy are critical for building and deploying AI-based detection systems.
Install and import TensorFlow for model training and inference
Use OpenCV for image and video processing tasks
Leverage NumPy for efficient numerical computations
Explore Matplotlib for data visualization and model evaluation
Acquiring and understanding a dataset is crucial for training effective AI models, involving sourcing relevant data and analyzing its structure.
Download a labeled accident detection dataset from a trusted source like Kaggle
Review dataset structure, including image formats and annotation files
Verify data quality and relevance for the accident detection task
Document dataset characteristics, such as size and class distribution
Visualizing and analyzing the dataset helps understand data distribution and identify patterns critical for model training.
Plot sample images with annotations to visualize accident scenarios
Analyze class distribution to check for imbalances in the dataset
Use histograms to explore pixel intensity distributions
Identify outliers or corrupted data for cleaning
Preprocessing ensures the dataset is standardized for model training, improving performance and convergence.
Normalize pixel values to a consistent range (e.g., 0 to 1) for faster training
Resize images to a uniform resolution compatible with the model architecture
Apply data augmentation techniques like rotation and flipping to increase diversity
Remove or correct corrupted images to ensure data quality
Label encoding and data preparation transform raw data into a format suitable for model training, ensuring compatibility with machine learning frameworks.
Convert categorical labels into numerical formats using one-hot encoding
Split dataset into training, validation, and test sets for evaluation
Ensure consistent label mapping across all data splits
Prepare data loaders for efficient batch processing during training
Visualizing training and validation data helps monitor model performance and detect issues like overfitting during training.
Plot training and validation loss curves to assess model convergence
Display sample predictions to verify model learning progress
Visualize accuracy trends across epochs for performance evaluation
Highlight discrepancies between training and validation metrics
Is This Course Right for You?
Kickstart your AI journey with structured, hands-on learning.
Build a portfolio that recruiters 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
Get hands-on experience with real-world projects designed to sharpen your technical skills and build your confidence. Each project is crafted to help you apply concepts practically, write cleaner code, and prepare for real developer challenges.
Highly recommended for small teams who seek to upgrade their time & perform.
₹ 6720 inclusive of GST ₹ 13999
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