Real-Time People Counting with YOLOv8, OpenCV, and Python

Real-Time People Entry and Exit Tracking for Effective Occupancy Management with Python & Computer Vision

What you'll learn
Understand the fundamentals of people entry and exit tracking and its importance in effective occupancy management in various settings.
Set up a Python development environment with essential libraries like Tkinter, OpenCV, and other tools for computer vision tasks.
Explore the concepts of object detection and how they can be applied to tracking people in video streams.
Learn how to perform people tracking using the YOLOv8 model, which is optimized for fast and efficient detection.
Load pre-trained YOLOv8 weights to perform people detection with high accuracy and efficiency.
Preprocess input images or live video feeds to ensure compatibility with the YOLOv8 model for optimal detection performance.
Visualize detection results by annotating video frames or images with bounding boxes and confidence scores, enhancing the interpretability of detection outputs.
Address common challenges in entry and exit tracking, such as detecting overlapping individuals, occlusions, and variations in movement patterns.
Understand how to apply AI-powered people entry and exit tracking systems for various occupancy management applications in public spaces, buildings,malls, etc.

Requirements
Basic understanding of Python programming (helpful but not mandatory).
A laptop or desktop computer with internet access[Windows OS with Minimum 4GB of RAM).
No prior knowledge of AI or Machine Learning is required—this course is beginner-friendly.
Enthusiasm to learn and build practical projects using AI and IoT tools.

Description
Welcome to the AI-Powered People Entry and Exit Tracking with YOLOv8 and Tkinter course! In this comprehensive hands-on course, you'll learn how to build a real-time people counting system using the powerful YOLOv8 algorithm and a Tkinter-based GUI for live tracking and visualization.This course focuses on leveraging pre-trained YOLOv8 models to count people entering and exiting designated areas. By the end of this course, you’ll have developed an AI-powered occupancy management system that provides real-time insights into foot traffic.● Set up a Python development environment and install essential libraries like OpenCV, and Tkinter for building your tracking system.● Use pre-trained YOLOv8 models to detect and track people, enabling accurate entry and exit counts in real-time.● Preprocess video streams to prepare for efficient object detection and implement inference with YOLOv8.● Design and implement a Tkinter-based GUI to visualize the live tracking output, displaying real-time counts of people entering and exiting.● Explore techniques to improve detection accuracy, addressing challenges like overlapping individuals, occlusions, and variations in movement.● Optimize the system for real-time performance, ensuring fast and efficient processing of live video streams.● Handle real-world challenges such as lighting variations, camera angles, and crowded environments to achieve robust tracking results.By the end of this course, you'll have a fully functional people counting system capable of tracking entry and exit in real-time and visualizing the data through an interactive Tkinter GUI. This project is perfect for applications like retail stores, event venues, or public spaces where effective occupancy management is critical.Whether you're a beginner or have experience with computer vision, this course provides hands-on knowledge in deploying object detection models, real-time tracking, and building intuitive GUIs, empowering you to create impactful AI-powered solutions. Enroll today and get started on your journey to smarter occupancy management!

Who this course is for
Students looking to dive into AI and learn practical applications in People In and Out Counting using Pre-trained Yolov8 Algorithm.
Working professionals wanting to upskill in AI, Machine Learning, and Python programming for real-world applications.
IoT enthusiasts who want to integrate AI into Internet of Things (IoT) solutions.
Aspiring developers aiming to build a career in AI, machine learning, or computer vision.