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tracking-keypoints

tracking-keypoints

Operate supervision keypoint containers, keypoint annotators, tracking IDs, and migration paths.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

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name: tracking-keypoints description: "Operate supervision keypoint containers, keypoint annotators, tracking IDs, and migration paths." disable-model-invocation: true metadata: disco-role: operating license: MIT

Tracking Keypoints

Use this sub-skill when a task involves supervision keypoint data, keypoint annotation, keypoint-to-detection conversion for tracking, persistent tracker_id workflows, line-crossing/counting that depends on tracker identity, or migration away from deprecated keypoint/tracker APIs.

Route here for

  • KeyPoints, keypoint_confidence, detection_confidence, visible, KeyPoints.merge, KeyPoints.with_nms, and KeyPoints.as_detections.
  • Keypoint adapters such as KeyPoints.from_ultralytics, from_inference, from_mediapipe, from_yolo_nas, from_detectron2, and from_transformers.
  • VertexAnnotator, EdgeAnnotator, VertexLabelAnnotator, VertexEllipseAreaAnnotator, VertexEllipseOutlineAnnotator, VertexEllipseHaloAnnotator, and the VertexEllipseAnnotator alias.
  • ByteTrack, ByteTrackTracker, tracker_id, track_objects, track labels, TraceAnnotator, DetectionsSmoother, and LineZone when persistent identity is central to the answer.
  • Deprecated supervision.keypoint, deprecated sv.ByteTrack, old ByteTrack threshold names, and the KeyPoints.confidence rename.

Route away

  • Build, filter, save, or reason about ordinary Detections, masks, polygon zones, line-zone geometry, sinks, or slicers when tracker continuity is not central: use detection-and-zones.
  • Non-keypoint detection/mask/label visualization and annotator catalog choices: use annotators.
  • Image/video I/O, primitive drawing, OpenCV backend diagnosis, GUI/window problems, and video codecs: use media-utils.
  • Dataset conversion or detection metric evaluation: use the corresponding dataset or metrics sub-skill.

Operating checklist

  1. Identify the active container. Pose/keypoint model outputs should become sv.KeyPoints; tracking, line crossing, traces, smoothing, and most labels operate on sv.Detections with tracker_id.
  2. Prefer sv.KeyPoints or supervision.key_points. Do not introduce new supervision.keypoint imports; that path is compatibility-only and scheduled for removal.
  3. Use keypoint_confidence for per-anchor scores and detection_confidence for whole-skeleton scores. Treat KeyPoints.confidence and confidence= as deprecated compatibility shims.
  4. Before keypoint annotation, decide whether missing/weak anchors should be hidden with key_points.visible. When visible is None, annotators treat anchors as visible except that all-zero coordinates are skipped.
  5. Before keypoint tracking, convert with key_points.as_detections(...), ensure the resulting detections have confidence scores and positive-area boxes, then run a tracker. Prefer the external ByteTrackTracker package when available; sv.ByteTrack is only a deprecated compatibility fallback in this version.
  6. Use the tracked Detections object for tracker labels, traces, detection smoothing, and LineZone.trigger(...). Missing tracker_id is the first thing to check when these workflows do nothing or raise.
  7. Put detailed API choices and recipes in tracking-keypoints.md, and failure repair steps in troubleshooting.md. Use the bundled draw_zones.py helper only for interactive polygon authoring in a GUI-capable environment.

Required context to keep in answers

  • Target package facts: supervision 0.31.0.dev0, Python >=3.10, base install pip install supervision.
  • Model adapters require their own model/framework packages; they are not part of the base supervision install.
  • ByteTrackTracker comes from the optional external trackers package. sv.ByteTrack still exists as a lazy compatibility export in this target, but it is deprecated and should not be presented as stable.
  • Native OpenCV is optional for the package. Interactive helpers require a GUI display and an OpenCV-compatible drawing/video backend; the documented fallback backend may render or decode differently from native OpenCV.
  • Do not recommend original repository docs, examples, tests, scripts, local checkout paths, or generated skill import steps as runtime dependencies.
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