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feat(tracking): add recursive lifecycle updates and quality diagnostics

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Implement the next tracker tranche around a recursive articulated state rather than per-frame ad hoc updates.

Track state now propagates full pose/velocity/shape covariance, uses process noise during prediction, and drives active-to-lost transitions from both miss counts and recursive score thresholds. The multiview update path replaces the generic SciPy least_squares call with a bounded LM/GN loop that returns parameter and beta covariance blocks, accepted-joint counts, mean reprojection error, and iteration diagnostics.

Lost-track handling is stricter and safer: proposal-based reacquisition now requires same-frame 2D support and articulated refinement before a track can return to active. Proposal clusters retain contributing detection indices, the tracker searches broadly within contributing views, and proposal-compatible lost frames are surfaced explicitly instead of silently reviving a track. Old scene JSONs with imgpaths now default to the RPT camera-pose convention so proposal reprojection gating works on the sample scenes.

Add ActualTest support diagnostics that summarize event counts, accepted support, reprojection quality, and tracker diagnostics, plus focused regressions for camera conventions, score-driven demotion, covariance behavior, proposal-compatible lost handling, and broader proposal-backed matching.
This commit is contained in:
crosstyan
2026-03-27 15:36:48 +08:00
parent 8085885a3a
commit 0bfeec77e4
10 changed files with 1883 additions and 257 deletions
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tests/test_tracker_single_person.py
+313 -2
View File
@@ -1,13 +1,25 @@
from pathlib import Path
from types import SimpleNamespace
import numpy as np
import pytest
pytest.importorskip("rpt")
from pose_tracking_exp.common.camera_math import project_pose
from pose_tracking_exp.common.joints import BODY20_INDEX_BY_NAME
from pose_tracking_exp.schema import CameraCalibration, CameraFrame, FrameBundle, ProposalCluster, SceneConfig, TrackerConfig
from pose_tracking_exp.tracking import PoseTracker
from pose_tracking_exp.schema import (
ActiveTrackState,
CameraCalibration,
CameraFrame,
FrameBundle,
PoseDetection,
ProposalCluster,
SceneConfig,
TRACK_COVARIANCE_DIMENSION,
TrackerConfig,
)
from pose_tracking_exp.tracking import PoseTracker, seed_state_from_pose3d
def _make_scene() -> SceneConfig:
@@ -89,6 +101,26 @@ def _make_proposal(root_x: float, *, score: float = 1.0) -> ProposalCluster:
source_views=frozenset({"cam0", "cam1"}),
support_size=2,
mean_score=score,
support_detection_indices={"cam0": (0,), "cam1": (0,)},
)
def _fake_detection() -> PoseDetection:
return PoseDetection(
bbox=np.asarray([0.0, 0.0, 1.0, 1.0], dtype=np.float64),
bbox_confidence=1.0,
keypoints=np.zeros((20, 3), dtype=np.float64),
)
def _detection_from_projection(projected: np.ndarray, *, confidence: float = 1.0) -> PoseDetection:
keypoints = np.zeros((20, 3), dtype=np.float64)
keypoints[:, :2] = projected[:, :2]
keypoints[:, 2] = confidence
return PoseDetection(
bbox=np.asarray([0.0, 0.0, 1.0, 1.0], dtype=np.float64),
bbox_confidence=confidence,
keypoints=keypoints,
)
@@ -147,6 +179,32 @@ def test_single_person_mode_reuses_lost_track_id(monkeypatch) -> None:
"_build_proposals",
lambda bundle, unmatched: proposals_by_bundle[bundle.bundle_index],
)
fake_detection = _fake_detection()
monkeypatch.setattr(
tracker,
"_proposal_support_matches",
lambda bundle, track, proposal, seeded_state: {"cam0": fake_detection, "cam1": fake_detection},
)
update_result = SimpleNamespace(
state=seed_state_from_pose3d(_make_proposal(0.05, score=0.96).pose3d),
parameter_covariance=np.eye(31, dtype=np.float64) * 0.1,
beta_covariance=np.eye(8, dtype=np.float64) * 0.01,
accepted_joint_masks={"cam0": np.ones((20,), dtype=bool), "cam1": np.ones((20,), dtype=bool)},
accepted_joint_counts_by_view={"cam0": 20, "cam1": 20},
accepted_joint_count=20,
accepted_view_count=2,
mean_reprojection_error=5.0,
lm_iterations=2,
)
monkeypatch.setattr(
tracker,
"_refine_track_state",
lambda track, predicted_state, matched: (
update_result,
np.full((20,), 9.0, dtype=np.float64),
{"cam0": np.full((20,), 9.0, dtype=np.float64), "cam1": np.full((20,), 9.0, dtype=np.float64)},
),
)
results = tracker.run([_make_bundle(0), _make_bundle(1), _make_bundle(2)])
@@ -154,3 +212,256 @@ def test_single_person_mode_reuses_lost_track_id(monkeypatch) -> None:
assert [track.track_id for track in results[1].lost_tracks] == [1]
assert [track.track_id for track in results[2].active_tracks] == [1]
assert tracker.diagnostics_snapshot().reacquisitions >= 1
def test_active_track_is_not_reseeded_from_proposals(monkeypatch) -> None:
tracker = PoseTracker(
_make_scene(),
TrackerConfig(
max_active_tracks=1,
tentative_min_age=1,
tentative_hits_required=1,
tentative_promote_score=0.0,
active_miss_to_lost=3,
proposal_min_score=0.5,
),
)
proposals_by_bundle = {
0: (_make_proposal(0.0, score=0.95),),
1: (_make_proposal(0.8, score=0.99),),
}
monkeypatch.setattr(
tracker,
"_build_proposals",
lambda bundle, unmatched: proposals_by_bundle[bundle.bundle_index],
)
results = tracker.run([_make_bundle(0), _make_bundle(1)])
assert [track.track_id for track in results[1].active_tracks] == [1]
active_track = results[1].active_tracks[0]
assert active_track.last_update_kind == "predict_only"
assert abs(float(active_track.skeleton.pose3d[BODY20_INDEX_BY_NAME["hip_middle"], 0])) < 0.2
assert not any(event.action == "proposal_reacquire" for event in results[1].update_events)
def test_lost_track_deleted_by_covariance_trace() -> None:
tracker = PoseTracker(_make_scene(), TrackerConfig(max_active_tracks=1, lost_covariance_trace_max=10.0))
proposal = _make_proposal(0.0, score=0.95)
tracker._lost[1] = ActiveTrackState(
track_id=1,
status="lost",
lost_age=1,
skeleton=seed_state_from_pose3d(proposal.pose3d),
covariance=np.eye(TRACK_COVARIANCE_DIMENSION, dtype=np.float64) * 1_000.0,
)
result = tracker.step(_make_bundle(0))
assert not result.lost_tracks
assert any(event.action == "deleted_lost" for event in result.update_events)
def test_track_beta_freezes_after_grace_update(monkeypatch) -> None:
tracker = PoseTracker(_make_scene(), TrackerConfig(max_active_tracks=1, beta_grace_frames=1))
proposal = _make_proposal(0.0, score=0.95)
skeleton = seed_state_from_pose3d(proposal.pose3d)
tracker._active[1] = ActiveTrackState(track_id=1, status="active", skeleton=skeleton, score=1.0)
fake_detection = PoseDetection(
bbox=np.asarray([0.0, 0.0, 1.0, 1.0], dtype=np.float64),
bbox_confidence=1.0,
keypoints=np.zeros((20, 3), dtype=np.float64),
)
monkeypatch.setattr(
tracker,
"_match_existing_tracks",
lambda bundle, predicted: ({1: {"cam0": fake_detection, "cam1": fake_detection}}, {"cam0": [], "cam1": []}),
)
updated_state = seed_state_from_pose3d(proposal.pose3d, beta=np.full((8,), 1.1, dtype=np.float64))
update_result = SimpleNamespace(
state=updated_state,
parameter_covariance=np.eye(31, dtype=np.float64) * 0.1,
beta_covariance=np.eye(8, dtype=np.float64) * 0.01,
accepted_joint_masks={"cam0": np.ones((20,), dtype=bool), "cam1": np.ones((20,), dtype=bool)},
accepted_joint_counts_by_view={"cam0": 20, "cam1": 20},
accepted_joint_count=20,
accepted_view_count=2,
mean_reprojection_error=4.0,
lm_iterations=1,
)
monkeypatch.setattr(
tracker,
"_refine_track_state",
lambda track, predicted_state, matched: (
update_result,
np.full((20,), 9.0, dtype=np.float64),
{"cam0": np.full((20,), 9.0, dtype=np.float64), "cam1": np.full((20,), 9.0, dtype=np.float64)},
),
)
monkeypatch.setattr(tracker, "_build_proposals", lambda bundle, unmatched: ())
result = tracker.step(_make_bundle(0))
assert result.active_tracks[0].beta_frozen
np.testing.assert_allclose(result.active_tracks[0].skeleton.beta, np.full((8,), 1.1, dtype=np.float64))
def test_active_track_demotes_to_lost_on_score_floor() -> None:
tracker = PoseTracker(
_make_scene(),
TrackerConfig(max_active_tracks=1, active_miss_to_lost=10, active_score_lost_threshold=0.0),
)
proposal = _make_proposal(0.0, score=0.95)
tracker._active[1] = ActiveTrackState(
track_id=1,
status="active",
score=0.1,
skeleton=seed_state_from_pose3d(proposal.pose3d),
covariance=np.eye(TRACK_COVARIANCE_DIMENSION, dtype=np.float64),
)
result = tracker.step(_make_bundle(0))
assert not result.active_tracks
assert [track.track_id for track in result.lost_tracks] == [1]
def test_proposal_compatible_lost_track_stays_lost_without_enough_support(monkeypatch) -> None:
tracker = PoseTracker(
_make_scene(),
TrackerConfig(max_active_tracks=1, active_miss_to_lost=1, lost_delete_age=10),
)
proposal = _make_proposal(0.0, score=0.95)
tracker._lost[1] = ActiveTrackState(
track_id=1,
status="lost",
lost_age=1,
score=1.0,
skeleton=seed_state_from_pose3d(proposal.pose3d),
covariance=np.eye(TRACK_COVARIANCE_DIMENSION, dtype=np.float64),
)
monkeypatch.setattr(tracker, "_build_proposals", lambda bundle, unmatched: (proposal,))
monkeypatch.setattr(tracker, "_proposal_support_matches", lambda bundle, track, proposal, seeded_state: {"cam0": _fake_detection()})
result = tracker.step(_make_bundle(0))
assert not result.active_tracks
assert [track.track_id for track in result.lost_tracks] == [1]
assert any(event.action == "proposal_compatible" for event in result.update_events)
def test_proposal_support_matches_search_all_view_detections() -> None:
scene = _make_scene()
tracker = PoseTracker(_make_scene(), TrackerConfig(max_active_tracks=1, lost_min_accepted_core_joints=2))
proposal = _make_proposal(0.0, score=0.95)
track = ActiveTrackState(track_id=1, status="lost", skeleton=seed_state_from_pose3d(proposal.pose3d))
seeded_state = seed_state_from_pose3d(proposal.pose3d)
projected_cam0 = project_pose(scene.cameras[0], seeded_state.pose3d)
projected_cam1 = project_pose(scene.cameras[1], seeded_state.pose3d)
good_cam0 = _detection_from_projection(projected_cam0)
good_cam1 = _detection_from_projection(projected_cam1)
bad_detection = _fake_detection()
bundle = FrameBundle(
bundle_index=0,
timestamp_unix_ns=0,
views=(
CameraFrame(
camera_name="cam0",
frame_index=0,
timestamp_unix_ns=0,
detections=(bad_detection, good_cam0),
source_size=(640, 480),
),
CameraFrame(
camera_name="cam1",
frame_index=0,
timestamp_unix_ns=0,
detections=(bad_detection, good_cam1),
source_size=(640, 480),
),
),
)
matched = tracker._proposal_support_matches(bundle, track, proposal, seeded_state)
assert matched["cam0"] is good_cam0
assert matched["cam1"] is good_cam1
def test_covariance_grows_on_predict_only_and_shrinks_on_update(monkeypatch) -> None:
tracker = PoseTracker(_make_scene(), TrackerConfig(max_active_tracks=1, active_miss_to_lost=10))
proposal = _make_proposal(0.0, score=0.95)
tracker._active[1] = ActiveTrackState(
track_id=1,
status="active",
score=1.0,
skeleton=seed_state_from_pose3d(proposal.pose3d),
covariance=np.eye(TRACK_COVARIANCE_DIMENSION, dtype=np.float64),
)
no_detection_bundle = _make_bundle(0)
predict_only_result = tracker.step(no_detection_bundle)
predict_only_cov_trace = float(np.trace(predict_only_result.active_tracks[0].covariance))
fake_detection = _fake_detection()
monkeypatch.setattr(
tracker,
"_match_existing_tracks",
lambda bundle, predicted: ({1: {"cam0": fake_detection, "cam1": fake_detection}}, {"cam0": [], "cam1": []}),
)
update_result = SimpleNamespace(
state=seed_state_from_pose3d(proposal.pose3d, beta=np.ones((8,), dtype=np.float64)),
parameter_covariance=np.eye(31, dtype=np.float64) * 0.01,
beta_covariance=np.eye(8, dtype=np.float64) * 0.001,
accepted_joint_masks={"cam0": np.ones((20,), dtype=bool), "cam1": np.ones((20,), dtype=bool)},
accepted_joint_counts_by_view={"cam0": 20, "cam1": 20},
accepted_joint_count=20,
accepted_view_count=2,
mean_reprojection_error=3.0,
lm_iterations=1,
)
monkeypatch.setattr(
tracker,
"_refine_track_state",
lambda track, predicted_state, matched: (
update_result,
np.full((20,), 9.0, dtype=np.float64),
{"cam0": np.full((20,), 9.0, dtype=np.float64), "cam1": np.full((20,), 9.0, dtype=np.float64)},
),
)
update_result_frame = tracker.step(_make_bundle(1))
updated_cov_trace = float(np.trace(update_result_frame.active_tracks[0].covariance))
assert predict_only_cov_trace > float(TRACK_COVARIANCE_DIMENSION)
assert updated_cov_trace < predict_only_cov_trace
def test_proposal_compatible_lost_track_gets_score_relief(monkeypatch) -> None:
tracker = PoseTracker(
_make_scene(),
TrackerConfig(
max_active_tracks=1,
active_miss_to_lost=1,
lost_delete_age=10,
lost_score_decay=1.0,
lost_score_miss_penalty=0.5,
proposal_compatible_score_relief=0.4,
),
)
proposal = _make_proposal(0.0, score=0.95)
tracker._lost[1] = ActiveTrackState(
track_id=1,
status="lost",
lost_age=1,
score=1.0,
skeleton=seed_state_from_pose3d(proposal.pose3d),
covariance=np.eye(TRACK_COVARIANCE_DIMENSION, dtype=np.float64),
)
monkeypatch.setattr(tracker, "_build_proposals", lambda bundle, unmatched: (proposal,))
monkeypatch.setattr(tracker, "_proposal_support_matches", lambda bundle, track, proposal, seeded_state: {})
result = tracker.step(_make_bundle(0))
assert result.lost_tracks[0].score > 0.4