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festifun/plan/lane-H-tracker.md
type-two 56e569fd7a Plan: Phase 6 spec + directives round 6 (M18-M24 Friend Tracks, lanes H/J/K) 
Markers/badges -> detection -> cross-view triangulation -> track ribbons +
follow-a-friend + LED badge hardware kit. Coordinator-merges policy this phase.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-17 18:28:21 +10:00

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Lane H — Tracker (M19 detection, M20 solving)

You turn wearable markers in the videos into 3D friend tracks. Backend only. Spec: 30-phase6.md M19/M20 + contracts #1–#5. Branch lane/h-tracker, own worktree.

What foundation3 gives you

  • Stubs tracker_detect.py / tracker_solve.py with the contracts in their docstrings; API (POST /api/tracks/solve etc.), CLI (track), DB helpers, and the synthetic fixture's two moving markers + data/work/track_truth.json ground truth — all already wired.
  • You fill the two stub bodies and write the two test files. Nothing else.

M19 — detection, the parts that bite

  • Sample frames with the frames.py machinery (~8 fps is plenty; document your rate).
  • Color mode: convert to HSV, gate on saturation AND value before hue (pitfall #2); connected components; centroid → normalized cx/cy. Hue buckets from contract #1 + FESTIVAL4D_MARKER_HUES.
  • Blink mode: track candidate bright blobs across the sampled window, integrate brightness per 133 ms bit window using the video's real fps (pitfall #3), correlate against the preamble, check parity, emit code:<id>.
  • Empty/dark/markerless video → valid-empty detections. Never crash.

M20 — solving, the parts that bite

  • t_video → t_global through the frozen helpers ONLY (pitfall #1).
  • Triangulation through geometry primitives exactly as resolve.py does — read it first.
  • Bucket ≈0.25 s; 2+ cameras → triangulate (record views, quality from residual); 1 camera → ray ∩ ground plane per contract #5 (views=1, quality ≤ 0.5).
  • Smooth per track; write through db helpers; re-solve replaces that marker's tracks (idempotent — don't stack duplicates on every run).

Acceptance (evidence in plan/status/lane-H.md)

  • Detection: ≥90 % recall on visible synthetic markers, ≤0.008 normalized center error; blink ID 5 decoded (test_tracker_detect.py)
  • Solve: median 3D error < 0.3 vs track_truth.json, ≥80 % coverage; single-view fallback test; idempotent re-solve; degradations (test_tracker_solve.py)
  • CLI + POST /api/tracks/solve run detect→solve end-to-end on the fixture
  • Suite ≥ foundation3's count + yours, green; stub-era assertions superseded by your landing → CR entry (CR-1/-4/-5 pattern)