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This script is adopted from the SORT script by Alex Bewley alex@bewley.ai
"""
from __future__ import print_function
import pdb
import pickle
import cv2
import numpy as np
from .association import *
from .embedding import EmbeddingComputer
from .cmc import CMCComputer
def k_previous_obs(observations, cur_age, k):
if len(observations) == 0:
return [-1, -1, -1, -1, -1]
for i in range(k):
dt = k - i
if cur_age - dt in observations:
return observations[cur_age - dt]
max_age = max(observations.keys())
return observations[max_age]
def convert_bbox_to_z(bbox):
"""
Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
[x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
the aspect ratio
"""
w = bbox[2] - bbox[0]
h = bbox[3] - bbox[1]
x = bbox[0] + w / 2.0
y = bbox[1] + h / 2.0
s = w * h # scale is just area
r = w / float(h + 1e-6)
return np.array([x, y, s, r]).reshape((4, 1))
def convert_bbox_to_z_new(bbox):
w = bbox[2] - bbox[0]
h = bbox[3] - bbox[1]
x = bbox[0] + w / 2.0
y = bbox[1] + h / 2.0
return np.array([x, y, w, h]).reshape((4, 1))
def convert_x_to_bbox_new(x):
x, y, w, h = x.reshape(-1)[:4]
return np.array([x - w / 2, y - h / 2, x + w / 2, y + h / 2]).reshape(1, 4)
def convert_x_to_bbox(x, score=None):
"""
Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
[x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
"""
w = np.sqrt(x[2] * x[3])
h = x[2] / w
if score == None:
return np.array([x[0] - w / 2.0, x[1] - h / 2.0, x[0] + w / 2.0, x[1] + h / 2.0]).reshape((1, 4))
else:
return np.array([x[0] - w / 2.0, x[1] - h / 2.0, x[0] + w / 2.0, x[1] + h / 2.0, score]).reshape((1, 5))
def speed_direction(bbox1, bbox2):
cx1, cy1 = (bbox1[0] + bbox1[2]) / 2.0, (bbox1[1] + bbox1[3]) / 2.0
cx2, cy2 = (bbox2[0] + bbox2[2]) / 2.0, (bbox2[1] + bbox2[3]) / 2.0
speed = np.array([cy2 - cy1, cx2 - cx1])
norm = np.sqrt((cy2 - cy1) ** 2 + (cx2 - cx1) ** 2) + 1e-6
return speed / norm
def new_kf_process_noise(w, h, p=1 / 20, v=1 / 160):
Q = np.diag(
(
(p * w) ** 2,
(p * h) ** 2,
(p * w) ** 2,
(p * h) ** 2,
(v * w) ** 2,
(v * h) ** 2,
(v * w) ** 2,
(v * h) ** 2,
)
)
return Q
def new_kf_measurement_noise(w, h, m=1 / 20):
w_var = (m * w) ** 2
h_var = (m * h) ** 2
R = np.diag((w_var, h_var, w_var, h_var))
return R
class KalmanBoxTracker(object):
"""
This class represents the internal state of individual tracked objects observed as bbox.
"""
count = 0
def __init__(self, bbox, delta_t=3, orig=False, emb=None, alpha=0, new_kf=False):
"""
Initialises a tracker using initial bounding box.
"""
# define constant velocity model
if not orig:
from .kalmanfilter import KalmanFilterNew as KalmanFilter
else:
from filterpy.kalman import KalmanFilter
self.new_kf = new_kf
if new_kf:
self.kf = KalmanFilter(dim_x=8, dim_z=4)
self.kf.F = np.array(
[
# x y w h x' y' w' h'
[1, 0, 0, 0, 1, 0, 0, 0],
[0, 1, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 1, 0],
[0, 0, 0, 1, 0, 0, 0, 1],
[0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 1],
]
)
self.kf.H = np.array(
[
[1, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0],
]
)
_, _, w, h = convert_bbox_to_z_new(bbox).reshape(-1)
self.kf.P = new_kf_process_noise(w, h)
self.kf.P[:4, :4] *= 4
self.kf.P[4:, 4:] *= 100
# Process and measurement uncertainty happen in functions
self.bbox_to_z_func = convert_bbox_to_z_new
self.x_to_bbox_func = convert_x_to_bbox_new
else:
self.kf = KalmanFilter(dim_x=7, dim_z=4)
self.kf.F = np.array(
[
# x y s r x' y' s'
[1, 0, 0, 0, 1, 0, 0],
[0, 1, 0, 0, 0, 1, 0],
[0, 0, 1, 0, 0, 0, 1],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1],
]
)
self.kf.H = np.array(
[
[1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
]
)
self.kf.R[2:, 2:] *= 10.0
self.kf.P[4:, 4:] *= 1000.0 # give high uncertainty to the unobservable initial velocities
self.kf.P *= 10.0
self.kf.Q[-1, -1] *= 0.01
self.kf.Q[4:, 4:] *= 0.01
self.bbox_to_z_func = convert_bbox_to_z
self.x_to_bbox_func = convert_x_to_bbox
# Attempt
# self.kf.P[2, 2] = 10000
# self.kf.R[2, 2] = 10000
self.kf.x[:4] = self.bbox_to_z_func(bbox)
self.time_since_update = 0
self.id = KalmanBoxTracker.count
KalmanBoxTracker.count += 1
self.history = []
self.hits = 0
self.hit_streak = 0
self.age = 0
"""
NOTE: [-1,-1,-1,-1,-1] is a compromising placeholder for non-observation status, the same for the return of
function k_previous_obs. It is ugly and I do not like it. But to support generate observation array in a
fast and unified way, which you would see below k_observations = np.array([k_previous_obs(...]]), let's bear it for now.
"""
# Used for OCR
self.last_observation = np.array([-1, -1, -1, -1, -1]) # placeholder
# Used to output track after min_hits reached
self.history_observations = []
# Used for velocity
self.observations = dict()
self.velocity = None
self.delta_t = delta_t
self.emb = emb
self.frozen = False
def update(self, bbox):
"""
Updates the state vector with observed bbox.
"""
if bbox is not None:
self.frozen = False
if self.last_observation.sum() >= 0: # no previous observation
previous_box = None
for dt in range(self.delta_t, 0, -1):
if self.age - dt in self.observations:
previous_box = self.observations[self.age - dt]
break
if previous_box is None:
previous_box = self.last_observation
"""
Estimate the track speed direction with observations \Delta t steps away
"""
self.velocity = speed_direction(previous_box, bbox)
"""
Insert new observations. This is a ugly way to maintain both self.observations
and self.history_observations. Bear it for the moment.
"""
self.last_observation = bbox
self.observations[self.age] = bbox
self.history_observations.append(bbox)
self.time_since_update = 0
self.history = []
self.hits += 1
self.hit_streak += 1
if self.new_kf:
R = new_kf_measurement_noise(self.kf.x[2, 0], self.kf.x[3, 0])
self.kf.update(self.bbox_to_z_func(bbox), R=R, new_kf=True)
else:
self.kf.update(self.bbox_to_z_func(bbox))
else:
self.kf.update(bbox, new_kf=self.new_kf)
self.frozen = True
def update_emb(self, emb, alpha=0.9):
self.emb = alpha * self.emb + (1 - alpha) * emb
self.emb /= np.linalg.norm(self.emb)
def get_emb(self):
return self.emb
def apply_affine_correction(self, affine):
m = affine[:, :2]
t = affine[:, 2].reshape(2, 1)
# For OCR
if self.last_observation.sum() > 0:
ps = self.last_observation[:4].reshape(2, 2).T
ps = m @ ps + t
self.last_observation[:4] = ps.T.reshape(-1)
# Apply to each box in the range of velocity computation
for dt in range(self.delta_t, -1, -1):
if self.age - dt in self.observations:
ps = self.observations[self.age - dt][:4].reshape(2, 2).T
ps = m @ ps + t
self.observations[self.age - dt][:4] = ps.T.reshape(-1)
# Also need to change kf state, but might be frozen
self.kf.apply_affine_correction(m, t, self.new_kf)
def predict(self):
"""
Advances the state vector and returns the predicted bounding box estimate.
"""
# Don't allow negative bounding boxes
if self.new_kf:
if self.kf.x[2] + self.kf.x[6] <= 0:
self.kf.x[6] = 0
if self.kf.x[3] + self.kf.x[7] <= 0:
self.kf.x[7] = 0
# Stop velocity, will update in kf during OOS
if self.frozen:
self.kf.x[6] = self.kf.x[7] = 0
Q = new_kf_process_noise(self.kf.x[2, 0], self.kf.x[3, 0])
else:
if (self.kf.x[6] + self.kf.x[2]) <= 0:
self.kf.x[6] *= 0.0
Q = None
self.kf.predict(Q=Q)
self.age += 1
if self.time_since_update > 0:
self.hit_streak = 0
self.time_since_update += 1
self.history.append(self.x_to_bbox_func(self.kf.x))
return self.history[-1]
def get_state(self):
"""
Returns the current bounding box estimate.
"""
return self.x_to_bbox_func(self.kf.x)
def mahalanobis(self, bbox):
"""Should be run after a predict() call for accuracy."""
return self.kf.md_for_measurement(self.bbox_to_z_func(bbox))
"""
We support multiple ways for association cost calculation, by default
we use IoU. GIoU may have better performance in some situations. We note
that we hardly normalize the cost by all methods to (0,1) which may not be
the best practice.
"""
ASSO_FUNCS = {
"iou": iou_batch,
"giou": giou_batch,
"ciou": ciou_batch,
"diou": diou_batch,
"ct_dist": ct_dist,
}
class Track:
def __init__(self, track_id, tlrb, score):
self.track_id = track_id
self.tlwh = [tlrb[0], tlrb[1], tlrb[2] - tlrb[0], tlrb[3] - tlrb[1]]
self.score = score
class OCSort(object):
def __init__(
self,
det_thresh,
max_age=30,
min_hits=3,
iou_threshold=0.3,
delta_t=3,
asso_func="iou",
inertia=0.2,
w_association_emb=0.75,
alpha_fixed_emb=0.95,
aw_param=0.5,
embedding_off=False,
cmc_off=True,
aw_off=False,
new_kf_off=False,
grid_off=False,
**kwargs,
):
"""
Sets key parameters for SORT
"""
self.max_age = max_age
self.min_hits = min_hits
self.iou_threshold = iou_threshold
self.trackers = []
self.frame_count = 0
self.det_thresh = det_thresh
self.delta_t = delta_t
self.asso_func = ASSO_FUNCS[asso_func]
self.inertia = inertia
self.w_association_emb = w_association_emb
self.alpha_fixed_emb = alpha_fixed_emb
self.aw_param = aw_param
KalmanBoxTracker.count = 0
# self.embedder = EmbeddingComputer(kwargs["args"].dataset, kwargs["args"].test_dataset, grid_off)
# self.cmc = CMCComputer()
self.embedding_off = embedding_off
self.cmc_off = cmc_off
self.aw_off = aw_off
self.new_kf_off = new_kf_off
self.grid_off = grid_off
def update(self, fdets, img):
"""
Params:
dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).
Returns the a similar array, where the last column is the object ID.
NOTE: The number of objects returned may differ from the number of detections provided.
"""
#####
remain_inds = fdets[:, 4] > self.det_thresh
dets, dets_embs = fdets[remain_inds, 0:5], fdets[remain_inds, 5:]
self.frame_count += 1
# CMC
if not self.cmc_off:
transform = self.cmc.compute_affine(img_numpy, dets[:, :4], tag)
for trk in self.trackers:
trk.apply_affine_correction(transform)
trust = (dets[:, 4] - self.det_thresh) / (1 - self.det_thresh)
af = self.alpha_fixed_emb
# From [self.alpha_fixed_emb, 1], goes to 1 as detector is less confident
dets_alpha = af + (1 - af) * (1 - trust)
# get predicted locations from existing trackers.
trks = np.zeros((len(self.trackers), 5))
trk_embs = []
to_del = []
ret = []
for t, trk in enumerate(trks):
pos = self.trackers[t].predict()[0]
trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
if np.any(np.isnan(pos)):
to_del.append(t)
else:
trk_embs.append(self.trackers[t].get_emb())
trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
# Shape = (num_trackers, 3, 512) if grid
trk_embs = np.array(trk_embs)
for t in reversed(to_del):
self.trackers.pop(t)
velocities = np.array([trk.velocity if trk.velocity is not None else np.array((0, 0)) for trk in self.trackers])
last_boxes = np.array([trk.last_observation for trk in self.trackers])
k_observations = np.array([k_previous_obs(trk.observations, trk.age, self.delta_t) for trk in self.trackers])
"""
First round of association
"""
matched, unmatched_dets, unmatched_trks = associate(
dets,
trks,
dets_embs,
trk_embs,
self.iou_threshold,
velocities,
k_observations,
self.inertia,
self.w_association_emb,
self.aw_off,
self.aw_param,
self.embedding_off,
self.grid_off,
)
for m in matched:
self.trackers[m[1]].update(dets[m[0], :])
self.trackers[m[1]].update_emb(dets_embs[m[0]], alpha=dets_alpha[m[0]])
"""
Second round of associaton by OCR
"""
if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:
left_dets = dets[unmatched_dets]
left_dets_embs = dets_embs[unmatched_dets]
left_trks = last_boxes[unmatched_trks]
left_trks_embs = trk_embs[unmatched_trks]
# TODO: maybe use embeddings here
iou_left = self.asso_func(left_dets, left_trks)
iou_left = np.array(iou_left)
if iou_left.max() > self.iou_threshold:
"""
NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may
get a higher performance especially on MOT17/MOT20 datasets. But we keep it
uniform here for simplicity
"""
rematched_indices = linear_assignment(-iou_left)
to_remove_det_indices = []
to_remove_trk_indices = []
for m in rematched_indices:
det_ind, trk_ind = unmatched_dets[m[0]], unmatched_trks[m[1]]
if iou_left[m[0], m[1]] < self.iou_threshold:
continue
self.trackers[trk_ind].update(dets[det_ind, :])
self.trackers[trk_ind].update_emb(dets_embs[det_ind], alpha=dets_alpha[det_ind])
to_remove_det_indices.append(det_ind)
to_remove_trk_indices.append(trk_ind)
unmatched_dets = np.setdiff1d(unmatched_dets, np.array(to_remove_det_indices))
unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))
for m in unmatched_trks:
self.trackers[m].update(None)
# create and initialise new trackers for unmatched detections
for i in unmatched_dets:
trk = KalmanBoxTracker(
dets[i, :], delta_t=self.delta_t, emb=dets_embs[i], alpha=dets_alpha[i], new_kf=not self.new_kf_off
)
self.trackers.append(trk)
i = len(self.trackers)
for trk in reversed(self.trackers):
if trk.last_observation.sum() < 0:
d = trk.get_state()[0]
else:
"""
this is optional to use the recent observation or the kalman filter prediction,
we didn't notice significant difference here
"""
d = trk.last_observation[:4]
if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):
# +1 as MOT benchmark requires positive
# ret.append(np.concatenate((d, [trk.id + 1])).reshape(1, -1))
ret.append(Track(trk.id + 1, d, 1))
i -= 1
# remove dead tracklet
if trk.time_since_update > self.max_age:
self.trackers.pop(i)
return ret
def dump_cache(self):
self.cmc.dump_cache()
self.embedder.dump_cache()
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