Welcome to the fascinating world of computer vision! Today, we will dive into how to leverage the YOLOv8 (You Only Look Once, version 8) framework for object tracking. With features that allow you to track objects in various media—from videos and images to real-time webcam feeds—this setup can be a valuable tool for developers and AI enthusiasts. Let’s break down the steps required to get you started!

Features of YOLOv8 Object Tracking

• Object Tracking: Use YOLOv8 to track multiple objects in real time.
• Different Color for Every Track: Easily distinguish between different tracked objects with individual colors.
• Multiple Media Supported: Track objects in videos, images, webcam feeds, and external camera streams.

Future Enhancements

• Selection of specific class IDs for tracking.
• Development of a user-friendly dashboard for more accessible monitoring and analysis.

Training YOLOv8 on Custom Data

If you want to train your own YOLOv8 model, you can find an insightful guide here: Train YOLOv8 on Custom Data.

Steps to Run the Code

Follow these steps to set up the YOLOv8 Object Tracking on your machine:

1. Clone the Repository:

   git clone https://github.com/RizwanMunawary/yolov8-object-tracking.git

2. Navigate to the Cloned Folder:

   cd yolov8-object-tracking

3. Install the Required Package:

   pip install ultralytics==8.0.0

4. Run the Tracking Command Based on Your Input Type:
   • For a video file:

     python yolov8/detect/detect_and_trk.py model=yolov8s.pt source=test.mp4 show=True

   • For an image file:

     python yolov8/detect/detect_and_trk.py model=yolov8m.pt source=path_to_image

   • For webcam:

     python yolov8/detect/detect_and_trk.py model=yolov8m.pt source=0 show=True

   • For an external camera:

     python yolov8/detect/detect_and_trk.py model=yolov8m.pt source=1 show=True

The output file will be created in the working directory named: runs/detect/train, with the same filename as your original media.

Understanding the Code Through Analogy

Think of the YOLOv8 object tracking process as a head chef running a kitchen:

• The **kitchen** represents your local directory hosting the YOLOv8 repository.
• The **chef’s recipe** signifies the tracking command that guides how to initiate the object tracking process.
• Each **ingredient** is like a video or image source; the chef needs to input these to create a finished dish (the tracking result).
• Finally, the **display of the final dish** is akin to the tracking output produced, which showcases how well the chef managed all ingredients—the various tracked objects!

Troubleshooting

If you encounter issues during the setup or implementation, here are some troubleshooting tips:

• Ensure Python and the required packages are correctly installed on your system.
• Check if the media file path is correct and accessible in your command.
• Verify that your webcam or external camera is working and recognized by your system.
• If you experience errors, reviewing the YOLOv8 GitHub repository’s issues section can provide valuable insights.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Results

Here are some results generated from YOLOv8 object tracking:

YOLOv8s Object Tracking	YOLOv8m Object Tracking

References

• Sort GitHub Repository
• Ultralytics GitHub Repository

Conclusion

Now that you have the knowledge and guidance to implement YOLOv8 object tracking effectively, you can explore new horizons in your computer vision projects!