Object Detection with YOLOv8 and Supervision

This project demonstrates object detection using the YOLOv8 model with video annotation, utilizing supervision for tracking and labeling, and opencv for video processing.

Installation

1. Clone the repository:

   git clone https://github.com/alihassanml/Object-Detection-Yolov8-Supervision.git
   cd Object-Detection-Yolov8-Supervision

2. Install the required dependencies:

   pip install -r requirements.txt

   Ensure you have opencv-python, supervision, and ultralytics installed.

Usage

1. Place your input video in the root directory of the project (e.g., Shopping.mp4).

2. Run the object detection script:

   python detect.py

3. The annotated video will be saved as Annotated_Shopping.mp4.

Description

• Object Detection: YOLOv8 is used to detect objects in a video.
• Tracking: ByteTrack is used for tracking objects across frames.
• Annotation: Bounding ellipses and labels are drawn for detected objects.
• Output: Annotated video is saved to a file.