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Deploy it on live camera feeds to detect available parking
spaces, track vehicle entry and exit, and optimize parking space
usage in real-time for public or private parking areas.
Beginner-friendly. Certificate included.
Startups
Microsoft
NVIDIA
By the end of this course, you'll have the skills to develop an AI-powered parking management system or secure freelance projects in this domain.
Explore how AI automates vehicle detection and parking space management in real time.
Use Python and OpenCV with YOLO for efficient vehicle and license plate detection.
Build a live system to monitor parking spaces using camera feeds.
Store vehicle data and manage parking logs in a database system.
Design a user-friendly GUI to manage and visualize parking system operations.
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
Real-time vehicle tracking enhances parking management by monitoring vehicle positions and optimizing space allocation using AI and computer vision.
Introduces AI-driven vehicle tracking for efficient parking management
Explores applications in smart parking and urban mobility solutions
Covers core concepts of computer vision and real-time processing
Outlines course objectives for building a parking management system
Configuring a Python environment ensures seamless development for building a vehicle tracking and parking management system.
Install Python and necessary libraries for computer vision tasks
Set up Visual Studio Code with Python extensions for coding
Create a virtual environment to isolate project dependencies
Test environment with a simple Python script execution
Organizing project folders and files streamlines development and ensures easy access to datasets, models, and scripts.
Create a structured folder hierarchy for datasets and code
Organize input images, annotations, and output files
Use consistent naming conventions for easy file management
Document folder structure for team collaboration
This system uses AI to detect vehicles and track parking slot occupancy in real-time, optimizing parking space management.
Uses computer vision to detect vehicles in parking areas
Tracks parking slot occupancy for real-time availability updates
Explores system scalability for large parking facilities
Addresses challenges like occlusion and varying lighting conditions
Flask provides a lightweight framework to create a web interface for the parking management system, enabling user interaction.
Set up a Flask application for the parking management system
Create routes to display parking slot availability
Integrate Flask with the vehicle detection model
Test the Flask app with a simple web interface
Developing a robust Flask backend enables real-time data processing and communication for the parking management system.
Develop API endpoints for vehicle detection and slot tracking
Integrate database to store parking slot status
Implement real-time updates for parking availability
Test backend functionality with sample API calls
This module focuses on implementing AI models to detect vehicles and track parking slot occupancy in real-time.
Train a model to detect vehicles in parking spaces
Implement logic to track occupancy status of parking slots
Integrate detection model with Flask backend for real-time updates
Validate detection accuracy with test video feeds
Installing required Python packages ensures the system has the tools needed for vehicle detection and web integration.
Install OpenCV for image and video processing
Set up Flask for web application development
Add NumPy for numerical computations
Install TensorFlow for model training and inference
Using Roboflow to annotate parking spaces with polygon coordinates enables accurate tracking of available parking slots.
Use Roboflow to annotate parking spaces with polygon coordinates
Export annotations for integration with detection model
Calculate parking slot availability based on polygon overlaps
Test polygon-based detection with sample images
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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