YOLOv8 Video Tracking

This project demonstrates how to use the YOLOv8 model for object tracking in a video using Python and OpenCV. The script loads a pre-trained YOLOv8 model, processes a video frame by frame, and displays the results with bounding boxes drawn around detected objects.

Requirements

• Python 3.7+
• OpenCV
• Ultralytics YOLOv8

Installation

1. Clone the repository:

   git clone https://github.com/eldan1z/object-detection-yolov8.git
   cd object-detection-yolov8

2. Install the necessary Python packages:

   pip install -r requirements.txt

3. Download the YOLOv8 model:

   The script uses the yolov8n.pt (nano version) model. It will be automatically downloaded when the script is run.

Usage

1. Place your video in the project directory:

   Make sure your video file is in the same directory as the script or provide the correct path to the video file.

2. Run the script:

   python yolo_video_tracking.py

   The script will start processing the video and display the frames with detected objects. Press q to stop the video.