YOLOv8 Human Labeling in MATLAB using Ground Truth Labeler

This guide explains how to integrate a pretrained YOLOv8 model with MATLAB's Ground Truth Labeler App to automatically generate human bounding box annotations and manually assign IDs using attributes.

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Requirements

• MATLAB R2023b or later
• Toolboxes:
  • Computer Vision Toolbox
  • Deep Learning Toolbox
  • Deep Learning Toolbox Converter for ONNX Model Format

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Steps to use YOLOv8 with Ground Truth Labeler in MATLAB

1. Download the repository

Download the YOLOv8 model integration from here:

• can use git clone https://github.com/LCAS/MATLAB_yolov8_human_labeling.git
• Or just download as a zip.

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2. Import the YOLO method

In MATLAB, following this video, import the YOLO method.

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3. Start auto labelling

The same as the previous method.

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4. Create a label

• Add a new rectangle ROI label named "human".

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5. Add an ID attribute

• Click the checkbox next to the label "human" in the label list.
• Click Attribute → Add a new attribute called ID.
• Set the Type to "List" and provide values like 1, 2, 3, ....

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6. Import the custom algorithm

• Go to Select Algorithm → click Import Algorithm → choose yolov8_label.m.

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7. Select the custom algorithm

• Choose "YOLOv8 Human Labeler" from the automation list.

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8. Run the automation

• Click Automate → then click Run. YOLOv8 will automatically draw bounding boxes for detected humans.

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9. Assign IDs to detections

For each frame:

• Click on each bounding box.
• In the right panel, assign the correct ID (e.g., 1 for person1, 2 for person2, etc.).

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10. Accept the results

• Once satisfied, click Accept to keep the generated labels.

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