Object Tracking and Movement Logging System

Overview

This project implements an object tracking and movement logging system using the YOLO (You Only Look Once) object detection model. The system tracks individuals in a video feed, monitors their movement across predefined areas, and logs this movement into an SQLite database.

Features

• Object Detection: Uses YOLO model to detect objects in video frames.
• Object Tracking: Tracks detected objects across frames with unique IDs.
• Movement Logging: Logs the movement of tracked individuals into an SQLite database.
• Area Monitoring: Monitors and logs if individuals move between predefined areas.

Dependencies

• Python 3.x
• OpenCV (cv2)
• Pandas (pandas)
• NumPy (numpy)
• SQLite (sqlite3)
• Ultralytics YOLO (ultralytics)

Setup

1. Clone the Repository

   git clone https://github.com/realtourani/Object-Tracking-and-Movement-Logging-System.git
   cd Object-Tracking-and-Movement-Logging-System-main
   

Install Dependencies

Install the required Python libraries using pip:

  pip install opencv-python-headless pandas numpy sqlite3 ultralytics

Configuration

1. Video Source

   Update the source variable in main.py with the path to your video file or the URL of your IP camera.

2. Define Tracking Areas

   Modify the area1 and area2 variables to define the areas you want to monitor. These areas are defined by a list of coordinates forming polygons.

Usage

1. Run the Script Execute the main script to start the object tracking and movement logging process:

  python main.py

2. View Output The processed video frames will be displayed in a window. The system will log the movement of individuals between the defined areas. Press Esc to exit the video feed window.

3. Access Logs Movement logs are stored in an SQLite database file named person_movement.db. The table movement_log contains records of person IDs, timestamps, and their movement states.