tracking

tracking

👋 hello

This script provides functionality for processing videos using YOLOv8 for object detection and Supervision for tracking and annotation.

💻 install

• clone repository and navigate to example directory

  git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
  cd supervision/examples/tracking

• setup python environment and activate it [optional]

  uv venv
  source .venv/bin/activate

• install required dependencies

  uv pip install -r requirements.txt

🛠️ script arguments

• ultralytics

  • --source_weights_path: Required. Specifies the path to the YOLO model's weights file, which is essential for the object detection process. This file contains the data that the model uses to identify objects in the video.

  • --source_video_path: Required. The path to the source video file to be processed. This is the video on which object detection and annotation will be performed.

  • --target_video_path: Required. The path where the processed video, with annotations added, will be saved. This is your output video file.

  • --confidence_threshold (optional): Sets the confidence level at which the model identifies objects in the video. Default is 0.3. A higher threshold makes the model more selective, while a lower threshold makes it more inclusive in identifying objects.

  • --iou_threshold (optional): Specifies the IOU (Intersection Over Union) threshold for the model, defaulting to 0.7. This parameter helps in differentiating between distinct objects, especially in crowded scenes.

• inference

  • --roboflow_api_key (optional): The API key for Roboflow services. If not provided directly, the script tries to fetch it from the ROBOFLOW_API_KEY environment variable. Follow this guide to acquire your API KEY.

  • --model_id (optional): Designates the Roboflow model ID to be used. The default value is "yolov8x-1280".

  • --source_video_path: Required. The path to the source video file to be processed. This is the video on which object detection and annotation will be performed.

  • --target_video_path: Required. The path where the processed video, with annotations added, will be saved. This is your output video file.

  • --confidence_threshold (optional): Sets the confidence level at which the model identifies objects in the video. Default is 0.3. A higher threshold makes the model more selective, while a lower threshold makes it more inclusive in identifying objects.

  • --iou_threshold (optional): Specifies the IOU (Intersection Over Union) threshold for the model, defaulting to 0.7. This parameter helps in differentiating between distinct objects, especially in crowded scenes.

⚙️ run

• inference

  python inference_example.py \
      --roboflow_api_key "ROBOFLOW_API_KEY" \
      --source_video_path input.mp4 \
      --target_video_path tracking_result.mp4

• ultralytics

  python ultralytics_example.py \
      --source_weights_path yolov8s.pt \
      --source_video_path input.mp4 \
      --target_video_path tracking_result.mp4

© license

This demo integrates two main components, each with its own licensing:

• ultralytics: The object detection model used in this demo, YOLOv8, is distributed under the AGPL-3.0 license. You can find more details about this license here.

• supervision: The analytics code that powers the zone-based analysis in this demo is based on the Supervision library, which is licensed under the MIT license. This makes the Supervision part of the code fully open source and freely usable in your projects.