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Johnny Fernandes
2026-05-11 10:35:48 +01:00
parent b457155538
commit fce0e0c786
27 changed files with 194 additions and 704 deletions
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herding/perception/sheep_tracker.py
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"""Multi-target tracker for LiDAR-detected sheep.
Greedy nearest-neighbour data association (with a distance gate) across
frames, plus a memory of last-seen positions for tracks that fall out
of the dog's FOV. Output is a ``{name: (x, y)}`` dict shaped exactly
like the receiver-based ``sheep_positions`` used previously by the
Webots controller and by the env, so Strömbom and Sequential can
consume it unchanged.
Penned-detection heuristic
--------------------------
Two ways a track is marked penned:
1. Its current estimated position is south of the gate plane and
within the gate column (the ``is_penned_position`` test the env
already uses on ground truth).
2. It hasn't been observed for ``STALE_STEPS`` and its last-seen
position was inside the gate-approach band — the dog's LiDAR can
only see ~2 m into the pen through the open gate, so a sheep
that disappeared near the entry has almost certainly entered.
Tracks marked penned are excluded from ``get_positions()`` (which is
what Strömbom consumes), matching the prior receiver-based behaviour.
"""
from __future__ import annotations
import math
from herding.world.geometry import MAX_SHEEP, in_pen, is_penned_position
GATE_M = 2.5 # m — primary NN gate (recent tracks)
REACQUIRE_GATE_M = 4.5 # m — wider gate for re-acquiring stale tracks (sheep moved during occlusion)
REACQUIRE_MIN_AGE = 20 # steps — only rebind via the wide gate if the track has been stale for this long
PENNED_GATE_M = 4.0 # m — wide gate for matching against already-penned tracks; the pen is small (3×7 m) so duplicates are easy without it
FORGET_STEPS = 200 # ~3.2 s — delete stale active tracks; tighter than 5 s to limit phantoms but long enough to bridge typical FOV gaps
MAX_ACTIVE_TRACKS = MAX_SHEEP # hard cap to the worst-case real flock size
# Penned tracks are never forgotten: sheep don't leave the pen, and
# losing the track makes the counter oscillate as the same sheep gets
# re-detected and counted multiple times.
class SheepTracker:
"""Online tracker with NN association and a forgetful memory.
Each track stores ``(x, y, last_seen_step, penned)``.
"""
def __init__(self, gate: float = GATE_M):
self.gate = gate
# tid → (x, y, last_seen_step, penned)
self._tracks: dict[int, tuple[float, float, int, bool]] = {}
self._next_id = 0
self.step = 0
def reset(self) -> None:
self._tracks.clear()
self._next_id = 0
self.step = 0
# ------------------------------------------------------------------
# Update
# ------------------------------------------------------------------
def update(self, detections: list[tuple[float, float]]) -> dict[str, tuple[float, float]]:
"""Fold a new set of detections in and return active positions."""
self.step += 1
det_used: set[int] = set()
updated_tids: set[int] = set()
# Pass 1: match against ACTIVE tracks first (oldest-seen-first so
# a re-emerging long-lost sheep grabs its old ID before a fresh
# neighbour does).
active_tids = [tid for tid, t in self._tracks.items() if not t[3]]
active_tids.sort(key=lambda tid: self._tracks[tid][2])
for tid in active_tids:
tx, ty, _, _ = self._tracks[tid]
best_j, best_d = -1, self.gate
for j, (dx, dy) in enumerate(detections):
if j in det_used:
continue
d = math.hypot(dx - tx, dy - ty)
if d < best_d:
best_d = d
best_j = j
if best_j >= 0:
dx, dy = detections[best_j]
self._tracks[tid] = (dx, dy, self.step, False)
det_used.add(best_j)
updated_tids.add(tid)
# Pass 1b: re-acquisition with a wider gate for tracks that have
# been stale for ≥ REACQUIRE_MIN_AGE steps. Sheep flee at
# ~0.6 m/s; over a 1–2 s occlusion (dog rotating or driving)
# they move enough that a fresh detection lies outside the
# primary GATE_M but is still clearly the same sheep. Without
# this, phantom tracks accumulate and corrupt the CoM.
for tid in active_tids:
if tid in updated_tids:
continue
tx, ty, last, _ = self._tracks[tid]
if (self.step - last) < REACQUIRE_MIN_AGE:
continue
best_j, best_d = -1, REACQUIRE_GATE_M
for j, (dx, dy) in enumerate(detections):
if j in det_used:
continue
d = math.hypot(dx - tx, dy - ty)
if d < best_d:
best_d = d
best_j = j
if best_j >= 0:
dx, dy = detections[best_j]
self._tracks[tid] = (dx, dy, self.step, False)
det_used.add(best_j)
updated_tids.add(tid)
# Pass 2: match remaining detections against PENNED tracks with
# a tighter gate. Without this, every frame near the gate spawns
# a fresh penned track for the same sheep, which under a long
# Webots run leads to thousands of phantom penned tracks.
penned_tids = [tid for tid, t in self._tracks.items() if t[3]]
for tid in penned_tids:
tx, ty, _, _ = self._tracks[tid]
best_j, best_d = -1, PENNED_GATE_M
for j, (dx, dy) in enumerate(detections):
if j in det_used:
continue
d = math.hypot(dx - tx, dy - ty)
if d < best_d:
best_d = d
best_j = j
if best_j >= 0:
dx, dy = detections[best_j]
self._tracks[tid] = (dx, dy, self.step, True)
det_used.add(best_j)
# Unmatched detections → new tracks. A detection that is already
# inside the pen is born "penned" so we don't accumulate active
# tracks for sheep that arrived in the pen during occlusion.
for j, (dx, dy) in enumerate(detections):
if j in det_used:
continue
penned = in_pen(dx, dy) or is_penned_position(dx, dy)
self._tracks[self._next_id] = (dx, dy, self.step, penned)
self._next_id += 1
# Promote active tracks to penned ONLY by geometric position
# (sheep is in the pen column south of the gate). The previous
# "stale + near gate" heuristic was firing on ordinary occlusion
# near the gate and creating phantom penned tracks.
for tid, (tx, ty, last, penned) in list(self._tracks.items()):
if penned:
continue
if is_penned_position(tx, ty):
self._tracks[tid] = (tx, ty, last, True)
# Forget stale ACTIVE tracks after FORGET_STEPS. Penned tracks
# are kept indefinitely — sheep can't escape the pen, so once a
# track is marked penned, that sheep is permanently penned.
for tid, (tx, ty, last, penned) in list(self._tracks.items()):
if penned:
continue
if (self.step - last) > FORGET_STEPS:
del self._tracks[tid]
# Hard cap on the active set. If we somehow have more than
# MAX_ACTIVE_TRACKS active tracks, drop the oldest-seen ones
# first — they are most likely false positives from world
# geometry (walls, gate posts) the env's raycaster doesn't
# model, and a bloated active set wrecks the downstream CoM.
active = [(tid, last) for tid, (_, _, last, p) in self._tracks.items()
if not p]
if len(active) > MAX_ACTIVE_TRACKS:
active.sort(key=lambda kv: kv[1]) # oldest-seen first
for tid, _ in active[: len(active) - MAX_ACTIVE_TRACKS]:
del self._tracks[tid]
return self.get_positions()
# ------------------------------------------------------------------
# Outputs
# ------------------------------------------------------------------
def get_positions(self) -> dict[str, tuple[float, float]]:
"""Active (not-yet-penned) tracks. Same shape as receiver dict."""
return {f"t{tid}": (x, y)
for tid, (x, y, _, penned) in self._tracks.items()
if not penned}
def get_penned_set(self) -> set[str]:
return {f"t{tid}" for tid, (_, _, _, penned) in self._tracks.items() if penned}
def n_active(self) -> int:
return sum(1 for _, _, _, penned in self._tracks.values() if not penned)
def n_penned(self) -> int:
return sum(1 for _, _, _, penned in self._tracks.values() if penned)