objectTrack

This Python script provides advanced object tracking using OpenCV. It supports multiple tracking algorithms, dynamic object reacquisition, and real-time feature comparison. The system allows users to select objects to track with a mouse, supports named tracking, and includes multiple modes for enhanced performance and reliability.

Features

• Instant Mode: Track objects immediately upon selection with a mouse.
• Named Tracking: Assign names to tracked objects for easier identification.
• Feature Comparison Mode: Reacquire lost objects using initial features captured during selection.
• Real-Time Multithreaded Processing: Capture and process frames in real-time for minimal latency.
• Flexible Tracking Algorithms: Choose from multiple tracking algorithms like CSRT, KCF, MIL, and more.

Installation

Prerequisites

Ensure you have Python 3.6 or newer installed. The script has been tested with Python 3.11.

Steps

1. Clone the Repository

   git clone https://github.com/small-cactus/objectTrack.git
   cd objectTrack

2. Set Up a Python Virtual Environment (optional but recommended)

   python -m venv venv
   venv\Scripts\activate      # On Windows
   source venv/bin/activate   # On macOS and Linux

3. Install Required Libraries

   pip install -r requirements.txt

   Libraries to be installed:

   opencv-contrib-python==4.10.0.82
   

Usage

Run the script from your command line:

python3 objectTracking.py

Key Bindings

• 'i': Toggle named tracking mode.
• 'f': Toggle feature comparison mode.
• 's': Start tracking (in non-instant mode).
• 'ESC': Reset all tracking.
• 'q': Quit the application.

Configuration

Adjust parameters based on your setup:

• Camera Index: Change cap = cv2.VideoCapture(0) if the default camera is not used. 0 is the value you should be changing.
• Resolution: Set the desired resolution with cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1920) and cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080).

License

This project is licensed under the MIT License - see the LICENSE file for details.