Our Instructors Collaborate With Top Tech Leaders
Startups
Microsoft
NVIDIA
Deploy on live camera feeds to track vehicle flow, detect
traffic patterns, and provide analytics for smart traffic
management and surveillance.
Beginner-friendly. Certificate included.
Startups
Microsoft
NVIDIA
By the end of this course, you'll have the skills to develop an AI-powered traffic monitoring system and pursue related job or freelance opportunities.
Explore how AI analyzes traffic patterns and vehicle movements in real time.
Use Python and OpenCV with advanced object detection libraries for vehicle tracking.
Build a system to monitor and analyze traffic flow using live video streams.
Store traffic data and generate logs for analysis in a database system.
Design a user-friendly GUI to manage and visualize traffic monitoring 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.
4 weeks of focused training with 8 lessons and 22 minutes of content
Real-time vehicle monitoring uses AI to optimize traffic flow, reduce congestion, and enhance road safety through advanced detection and tracking.
Introduces AI-driven vehicle monitoring for efficient traffic management
Explores applications in urban planning and traffic optimization
Covers basics of computer vision for vehicle detection and tracking
Outlines course objectives and structure for practical implementation
This system leverages AI to detect vehicles and monitor traffic patterns in real-time, enabling efficient traffic management and congestion control.
Uses computer vision to identify and track vehicles in video streams
Monitors traffic flow for real-time congestion analysis and alerts
Explains system architecture for scalable traffic monitoring deployment
Addresses challenges like occlusion and varying traffic conditions
Essential Python packages like YOLOv8, OpenCV, and Tkinter are key to building and visualizing real-time vehicle monitoring systems.
Install YOLOv8 for advanced vehicle detection and tracking
Use OpenCV for processing video feeds and image data
Integrate Tkinter for creating a user-friendly interface
Verify package installations with a sample script execution
Enabling user input and video file selection allows the system to process specific video feeds for real-time vehicle monitoring.
Implement a file dialog for users to select video files
Validate selected video formats for compatibility with OpenCV
Configure user input to specify monitoring parameters
Test file selection with a sample video input
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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