YOLOv8 Object Detection and Tracking 🚀

This project demonstrates the use of the YOLOv8 model for real-time object detection and tracking in videos. It utilizes the Ultralytics YOLOv8 model and OpenCV for video processing.

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📝 Requirements

Before running the project, ensure that you have the following installed:

• Python 3.7+
• YOLOv8 (from ultralytics)
• OpenCV

📦 Install Dependencies

Install the necessary dependencies using pip:

pip install ultralytics opencv-python

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🗂️ Project Structure

1. yolov8n.pt – Pre-trained YOLOv8 model file.
2. test.mp4 – Sample video file for object detection and tracking (optional, can be replaced with your own video).
3. main.py – Python script implementing object detection and tracking using YOLOv8 and OpenCV.

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🚀 How to Run

1. Clone the repository or download the project files to your local machine.

2. Ensure you have the YOLOv8 model weights (yolov8n.pt) downloaded from the Ultralytics YOLOv8 repository, or use a different YOLOv8 model variant if needed.

3. Prepare a video or webcam input:

   • Replace the video path in the script with your video file, or use 0 for webcam input.

4. Run the script:

   python main.py

   This will start processing the video, displaying the frames with object detection and tracking results. Press q to exit the video window.

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🧑‍💻 Code Explanation

- Model Loading

The YOLOv8 model is loaded with the following line:

model = YOLO('yolov8n.pt')

This loads the pre-trained YOLOv8 model for object detection and tracking.

- Video Capture

A video is loaded using OpenCV's VideoCapture function:

cap = cv2.VideoCapture(video_path)

- Object Detection & Tracking

Each frame of the video is processed in a loop where the YOLOv8 model detects and tracks objects:

results = model.track(frame, persist=True)

- Visualization

The results are visualized using OpenCV's imshow function, which displays the frames with bounding boxes and object labels:

cv2.imshow('frame', frame_)

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🛠️ Key Features

• Object Detection: Detects objects in each frame of the video.
• Object Tracking: Tracks detected objects throughout the video.
• Real-time Visualization: Displays the results on the screen with bounding boxes and labels.
• Customizable: Easily replace the video input or use a webcam for real-time detection.

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⚠️ Known Issues

• When using a webcam, ensure that the correct camera is selected in the cv2.VideoCapture() function.
• The performance of the model may vary depending on the hardware.

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