Revise the contents of the 'How to Track Objects' documentation covering the use of supervision for object tracking in video analysis. The revision offers a more detailed guide, including running inference, tracking objects, annotating videos with unique tracking IDs, and annotating videos with traces. The new guide provides a structured walkthrough and tutorial on using Supervision's functionalities, aimed at helping users to annotate videos more effectively and understand the movement patterns and interactions of objects in the videos.
This commit clarifies the process of object detection and annotation using Supervision and the Ultralytics YOLOv8 model. The changes also include updated instructions on how to load model predictions into Supervision and using them to annotate images, with links to the methods used in the process and the available options. An image showing the resulting annotated image has also been added for better understanding. The changes are shaped to make the documentation more informative, concise, and easy to follow.
Moved advanced filtering documentation to `how_to` directory from `quickstart` to make way for more comprehensive starter guide. Reorganized MkDocs configuration accordingly. Formatted some code in `classification/core.py` and `dataset/core.py` for better readability. Added placeholder files in `how_to` for future guides on object detection, video processing, object tracking, model evaluation, and detections filtering.
Piotr Skalski
pre-commit-ci[bot]
James Gallagher
Rajarshi Misra