🎥 Real-Time Video Analysis for Object Detection and Motion Tracking
📌 Abstract
In recent years, video-based analysis systems have become pivotal in fields such as surveillance, traffic management, and activity recognition. This project introduces an efficient and scalable real-time object detection and motion tracking system powered by advanced deep learning models and computer vision techniques. By integrating YOLOv5, OpenCV, and a user-friendly web-based interface, the system can identify and track objects in live or recorded video streams with high accuracy and minimal latency.

As a 4th-year student at the Tashkent University of Information Technologies, studying in the Artificial Intelligence direction under the Faculty of Computer Engineering, this project represents one of my early ventures into AI and computer vision. It combines my academic learning with practical application to solve real-world challenges effectively.

🔍 Motivation
With the exponential growth of video data, there is an increasing demand for intelligent systems capable of:

Accurately identifying objects in dynamic, real-world environments.
Tracking object movement to analyze motion patterns.
Processing videos in real-time, ensuring scalability and efficiency for practical use cases.
Traditional systems often struggle with computational bottlenecks and limited accuracy. This project leverages state-of-the-art AI tools to address these shortcomings and provide a robust solution.

🎯 Project Objectives
Real-Time Object Detection: Utilize the pretrained YOLOv5 model for fast and accurate object identification.
Motion Tracking: Implement tracking algorithms using OpenCV to monitor object movement across video frames.
Web-Based Integration: Develop an intuitive user interface where users can upload videos and view results seamlessly.

🛠️ Technologies Used
YOLOv5 🧠 – State-of-the-art deep learning model for object detection.
PyTorch ⚡ – A robust deep learning framework for model deployment.
OpenCV 👁️ – For efficient real-time video and image processing.
MoviePy 🎬 – To handle video input/output tasks.
Web Interface 🌐 – Enables user accessibility and real-time result visualization.

Screenshots of Project:

Full details are available on my GitHub page: https://github.com/humoyun200108/Video-analysis-and-motion-tracking.git

💡 Applications
This system has significant practical implications, including:

Surveillance Systems: Monitoring public or private spaces for security.
Traffic Management: Tracking vehicles and analyzing movement patterns.
Activity Recognition: Identifying human behavior in controlled environments (e.g., workplaces, homes).

👨‍🎓 About Me
I am currently a 4th-year student at the Tashkent University of Information Technologies (TUIT), studying under the Faculty of Computer Engineering with a specialization in Artificial Intelligence. This project is one of my initial steps into AI research and real-world application development. My aim is to contribute to the evolving field of computer vision by solving real-world problems with innovative solutions.

🚀 Future Enhancements
This project serves as a foundation for future developments, such as:

Integrating real-time video streaming capabilities for live analysis.
Expanding model performance using advanced architectures like YOLOv7 or transformer-based vision models.
Deploying the system on cloud platforms for broader accessibility and scalability.

📝 Conclusion
This project demonstrates the power of deep learning and computer vision in solving practical challenges. By combining robust technologies with an intuitive interface, the system offers a versatile solution for real-time video analysis. It also reflects my commitment to advancing AI-driven applications as part of my academic and professional journey.