Vehicle Person and Object Detection and Counting System

Project Overview:

This project leverages the YOLO11 (You Only Look Once) deep learning model to perform real-time object detection and counting in video feeds. The system is capable of detecting and counting vehicles, people, and other objects in live streams (webcam) or pre-recorded video files. By applying computer vision techniques with YOLO11, the project aims to provide accurate and efficient detection, counting, and tracking of objects across multiple video frames.

Key Features:

• Real-time Object Detection: The system detects and tracks multiple object classes (vehicles, people, and others) in real-time.
• Object Counting: Counts the total number of detected vehicles, people, and other objects in a given video feed or webcam stream.
• Live and Offline Processing: Supports both live webcam feeds and video file uploads for offline detection and counting.
• Tracking and Visualization: Objects are detected with bounding boxes and class labels. Each detected object is assigned a unique tracking ID, making it easier to track them across frames.
• Web Interface: A user-friendly web interface built with Flask allows users to upload videos or view live object detection from a webcam. The results, including object counts, are displayed in real-time.

Technologies Used:

• YOLO11: A state-of-the-art object detection model known for its speed and accuracy.
• OpenCV: Used for capturing and processing video frames.
• Flask: A lightweight Python web framework to build the frontend and serve video streams.
• Python: The main programming language for developing the backend of the project.

Applications:

1. Traffic Surveillance: Automatically detect and count vehicles in real-time, useful for traffic monitoring, management, and analysis.
2. Security Monitoring: Detect and count people in monitored areas, helping with security surveillance and crowd management.
3. Smart Cities: Collect real-time data on vehicle and pedestrian traffic for urban planning and infrastructure development.
4. Retail/Inventory Management: Count various objects in warehouses or retail environments, assisting with inventory tracking and management.

Conclusion:

This YOLO11-based Vehicle, Person, and Object Detection and Counting System provides a robust and scalable solution for real-time object detection and counting. By combining deep learning with computer vision techniques, it enables accurate object identification and tracking across various use cases, from traffic surveillance to security and inventory management.