Our Instructors Collaborate With Top Tech Leaders
Startups
Microsoft
NVIDIA
Deploy on camera feeds to track vehicles, calculate their speed,
and monitor traffic compliance for road safety and law
enforcement applications.
Beginner-friendly. Certificate included.
Startups
Microsoft
NVIDIA
By the end of this course, you'll have the skills to develop AI-based vehicle speed tracking systems and pursue related job opportunities or freelance projects.
Explore how AI detects and tracks vehicle speeds in real time using video feeds.
Use Python and OpenCV with AI libraries for accurate vehicle detection and speed calculation.
Build a system to detect and calculate vehicle speeds from live video streams.
Store vehicle speed data and maintain logs in a database system.
Design a user-friendly GUI to monitor and manage speed tracking data.
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
The Smart Vehicle Speed Tracking System uses AI and computer vision to monitor vehicle speeds in real-time, enhancing road safety and traffic management.
Introduces AI-driven vehicle speed tracking for real-time monitoring
Explores applications in traffic enforcement and accident prevention
Outlines the role of computer vision in detecting and tracking vehicles
Provides an overview of the course structure and learning objectives
Configuring a Python development environment ensures all necessary tools and libraries are ready for building the speed tracking system.
Install Python and set up a virtual environment for dependency management
Configure an IDE like VS Code with Python extensions for efficient coding
Test the environment with a basic Python script execution
Ensure compatibility with machine learning and computer vision libraries
This system leverages AI to detect vehicles and calculate their speeds using video feeds, enabling real-time traffic monitoring.
Uses YOLOv8 for accurate vehicle detection in video streams
Calculates vehicle speed through frame-by-frame tracking analysis
Discusses system architecture for scalable traffic monitoring
Addresses challenges like varying camera angles and lighting conditions
Essential Python packages like YOLOv8, OpenCV, and Tkinter are key to building and visualizing the vehicle speed tracking system.
Install YOLOv8 for vehicle detection and tracking
Use OpenCV for processing video frames and drawing bounding boxes
Integrate Tkinter for creating a real-time GUI dashboard
Explore NumPy for efficient numerical calculations in speed tracking
Calibration aligns pixel-based measurements with real-world units, ensuring accurate speed calculations for tracked vehicles.
Calibrate camera parameters to map pixels to real-world distances
Use reference objects to establish a distance-to-pixel ratio
Account for camera angle and perspective distortions in calibration
Test calibration accuracy with known distances in sample videos
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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