Our Instructors Collaborate With Top Tech Leaders
Startups
Microsoft
NVIDIA
Create an intelligent occupancy monitoring system using YOLO
object detection and real-time video processing. Track the
number of people entering and exiting a space with accurate,
dynamic counting and occupancy updates.
Advanced-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.
Discover how computer vision systems can monitor and count objects as they enter and exit a defined area in real time.
Apply Python and OpenCV to build automated entry-exit tracking systems, using object detection models to reliably count people or vehicles.
Build interactive systems that process live video to instantly update entry, exit, and current occupancy stats.
Store entry and exit records with timestamps and relevant snapshots in a structured database for detailed analysis.
Create an interactive GUI to display live video, real-time occupancy count, and a log of entry/exit events.
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 People Tracking uses AI to monitor and count individuals in spaces, optimizing occupancy management for safety and efficiency.
Introduces AI-driven occupancy tracking for real-time monitoring
Explores applications in retail, offices, and public spaces
Outlines the role of computer vision in people detection and tracking
Provides an overview of the course structure and objectives
Setting up a Python environment is essential for developing and testing people tracking systems efficiently.
Install Python and verify compatibility with required libraries
Set up an IDE like Visual Studio Code with Python extensions
Create a virtual environment to manage project dependencies
Test the setup with a basic Python script execution
YOLOv8 is a state-of-the-art object detection algorithm, ideal for real-time people tracking due to its speed and accuracy.
Learn the architecture and improvements of YOLOv8 over previous versions
Understand how YOLOv8 detects and tracks objects in real-time
Explore its suitability for people counting and occupancy tracking
Review the role of anchor-free detection in YOLOv8’s performance
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Essential Python packages like Ultralytics YOLO, OpenCV, and NumPy are critical for building a robust people tracking system.
Install Ultralytics YOLO for YOLOv8 model implementation
Use OpenCV for image processing and visualization tasks
Leverage NumPy for efficient numerical operations
Explore Tkinter for building a user interface for real-time tracking
Understanding key variables in YOLOv8 is crucial for configuring the model for accurate people detection and tracking.
Explore confidence thresholds for filtering detections
Understand IoU (Intersection over Union) for bounding box accuracy
Configure tracking parameters for consistent object identification
Review class IDs specific to people detection in YOLOv8
Implementing the logic for counting people involves creating functions to track individuals crossing designated lines or zones.
Design functions to count people entering and exiting a defined area
Implement logic to track object movement direction across lines
Handle edge cases like multiple people crossing simultaneously
Test counting logic with sample detection outputs
Line coordinates define boundaries for tracking people movement, enabling accurate in-and-out counting for occupancy monitoring.
Define line coordinates to mark entry and exit zones
Access coordinates programmatically for integration with tracking logic
Validate line placement for optimal tracking accuracy
Adjust coordinates based on camera angle and scene layout
Using YOLOv8 for model inference enables real-time people detection and counting in video streams or images.
Load a pre-trained YOLOv8 model for people detection
Process video frames to detect and track individuals
Integrate counting logic with YOLOv8 detection outputs
Test inference on sample videos to validate counting accuracy
Tkinter provides a simple GUI framework to display real-time people counting results and system status.
Create a Tkinter window to display counting metrics
Integrate real-time video feed or detection outputs in the GUI
Update counters dynamically for in-and-out movements
Test the GUI with sample tracking data for usability
Installing the right packages ensures the people counting system runs smoothly with all necessary dependencies.
Install dependencies like Ultralytics, OpenCV, and Tkinter
Verify package versions for compatibility with YOLOv8
Use pip to manage installations within a virtual environment
Test package functionality with a basic script execution
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.
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