-
Notifications
You must be signed in to change notification settings - Fork 5
/
tracker.py
242 lines (198 loc) · 8.79 KB
/
tracker.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
from __future__ import print_function
import numpy as np
from sklearn.utils.linear_assignment_ import linear_assignment
from filterpy.kalman import KalmanFilter
def iou_tracker(bb_test, bb_gt):
xx1 = np.maximum(bb_test[0], bb_gt[0])
yy1 = np.maximum(bb_test[1], bb_gt[1])
xx2 = np.minimum(bb_test[2], bb_gt[2])
yy2 = np.minimum(bb_test[3], bb_gt[3])
w = np.maximum(0., xx2 - xx1)
h = np.maximum(0., yy2 - yy1)
wh = w * h
iou = wh / ((bb_test[2] - bb_test[0]) * (bb_test[3] - bb_test[1]) + (
bb_gt[2] - bb_gt[0]) * (bb_gt[3] - bb_gt[1]) - wh)
return iou
def convert_bbox_to_z(bbox):
w = bbox[2] - bbox[0]
h = bbox[3] - bbox[1]
x = bbox[0] + w / 2
y = bbox[1] + h / 2
s = w * h
r = w / float(h)
return np.array([x, y, s, r]).reshape((4, 1))
def convert_x_to_bbox(x, score=None):
w = np.sqrt(x[2] * x[3])
h = x[2] / w
if score is None:
return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2.]).reshape((1, 4))
else:
return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2., score]).reshape((1, 5))
class KalmanBoxTracker(object):
count = 0
def __init__(self, bbox, min_hits):
self.kf = KalmanFilter(dim_x=7, dim_z=4)
self.kf.F = np.array( # state transistion matrix
[[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( # measurement function
[[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. # measurement uncertainty / noise
self.kf.P[4:, 4:] *= 1000. # covariance matrix
self.kf.P *= 10.
self.kf.Q[-1, -1] *= 0.01 # process uncertainty / noise
self.kf.Q[4:, 4:] *= 0.01
self.kf.x[:4] = convert_bbox_to_z(bbox) # filter state estimate
self.time_since_update = 0
self.id = KalmanBoxTracker.count
KalmanBoxTracker.count += 1
self.history = []
self.hits = 0
self.hit_streak = 0
self.age = 0
self.vip = False
self.min_hits = min_hits
self.previous = self.kf.x
self.skip_frame = 0
self.obj_speed = 0
self.max_age_ = 0
self.obj_delete = False
def update(self, bbox):
self.time_since_update = 0
self.history = []
self.hits += 1
self.hit_streak += 1
self.kf.update(convert_bbox_to_z(bbox))
self.previous = self.kf.x
self.obj_delete = False
self.max_age_, self.obj_speed = self.maximum_age()
if self.hit_streak >= self.min_hits:
self.vip = True
def predict(self):
if (self.kf.x[6] + self.kf.x[2]) <= 0:
self.kf.x[6] *= 0.0
self.previous = self.kf.x
self.kf.predict()
self.age += 1
if self.time_since_update > 0:
self.hit_streak = 0
self.time_since_update += 1
self.history.append(convert_x_to_bbox(self.kf.x))
self.obj_delete = True
if self.time_since_update == 0:
self.max_age_ = 0
self.obj_speed = 0
return self.history[-1]
def get_state(self):
return convert_x_to_bbox(self.kf.x)
def maximum_age(self):
obj_speed = np.sqrt(self.kf.x[4] ** 2 + self.kf.x[5] ** 2) * 10
if obj_speed < 10:
self.skip_frame = 100 / (obj_speed + 0.01)
# self.skip_frame = 10 * np.exp(-obj_speed)
elif obj_speed < 100:
self.skip_frame = 150 / (obj_speed + 0.01)
else:
self.skip_frame = 10
if self.skip_frame < 3:
self.skip_frame = 3
if self.skip_frame > 20:
self.skip_frame = 20
return int(self.skip_frame), obj_speed
def associate_detections_to_trackers(detections, trackers, iou_threshold=0.3):
if len(trackers) == 0:
return np.empty((0, 2), dtype=int), np.arange(len(detections)), np.empty((0, 5), dtype=int)
iou_matrix = np.zeros((len(detections), len(trackers)), dtype=np.float32)
for d, det in enumerate(detections):
for t, trk in enumerate(trackers):
iou_matrix[d, t] = iou_tracker(trk, det)
# Solve the linear assignment problem using the Hungarian algorithm
# The problem is also known as maximum weight matching in bipartite graphs. The method is also known as the
# Munkres or Kuhn-Munkres algorithm.
matched_indices = linear_assignment(-iou_matrix)
unmatched_detections = []
for d, det in enumerate(detections):
if d not in matched_indices[:, 0]:
unmatched_detections.append(d) # store index
unmatched_trackers = []
for t, trk in enumerate(trackers):
if t not in matched_indices[:, 1]:
unmatched_trackers.append(t) # store index
matches = []
for m in matched_indices:
if iou_matrix[m[0], m[1]] < iou_threshold:
unmatched_detections.append(m[0])
unmatched_trackers.append(m[1])
else:
matches.append(m.reshape(1, 2))
if len(matches) == 0:
matches = np.empty((0, 2), dtype=int)
else:
matches = np.concatenate(matches, axis=0)
return matches, np.array(unmatched_detections), np.array(unmatched_trackers)
class Tracker(object):
def __init__(self, max_age=15, min_hits=1, det_confidence=0.5):
self.max_age = max_age # 保持多少帧,我们允许多少帧没有检测到 mis_detection frame
self.min_hits = min_hits # 检测多少帧后就稳定了要开始做tracking,即消除false detection
self.trackers = []
self.frame_count = 0
self.det_confidence = det_confidence
def update(self, dets):
self.frame_count += 1
trks = np.zeros((len(self.trackers), 5))
to_del = []
ret1 = []
for t, trk in enumerate(trks): # t: index, trk: content
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)
trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
for t in reversed(to_del):
self.trackers.pop(t)
matched, unmateched_dets, unmatched_trks = associate_detections_to_trackers(dets, trks)
for t, trk in enumerate(self.trackers):
if t not in unmatched_trks:
d = matched[
np.where(matched[:, 1] == t)[0], 0] # np.where returns two array, one is row index, another
# is column. [[]]->[] dimension changed from (a,b) to (a)
trk.update(dets[d, :][0])
for i in unmateched_dets:
trk = KalmanBoxTracker(dets[i, :], min_hits=self.min_hits)
self.trackers.append(trk)
i = len(self.trackers)
# print("============================================")
for trk in reversed(self.trackers):
d = trk.get_state()[0] # trk.get_state() is [[]], shape is (1,4), trk.get_state()[0] is [] (shape: (4,))
# [[1,2]] is a 2d array, shape is (1,2), every row has two elements
# [1,2] is a one-d array, shape is (2,), this array has two elements
# [[1],[2]] is a 2d array, shape is (2,1) every row has one elements
self.max_age = trk.max_age_
if trk.obj_delete and np.abs(trk.previous[0] - d[0] > 70):
trk.time_since_update = 20
for trk1 in self.trackers:
d1 = trk1.get_state()[0]
over_lap = iou_tracker(d, d1)
if trk1.obj_delete is False and trk.obj_delete and ((d[0] > d1[0] and d[1] > d1[1] and d[2] + d[0] < d1[
2] + d1[0] and d[3] + d[1] < d1[3] + d1[1]) or (
d[0] > d1[0] and d[1] > d1[1] and d[2] < d1[2] and d[3] < d1[3])):
trk.time_since_update = 20
if trk1.obj_delete is False and trk.obj_delete and (over_lap >= 0.3 and over_lap <= 0.99):
trk.time_since_update = 20
if (trk.time_since_update < self.max_age) and ((trk.hit_streak >= self.min_hits) or trk.vip):
ret1.append(np.concatenate((d, [trk.id + 1])).reshape(1, -1))
i -= 1
if trk.time_since_update > self.max_age:
self.trackers.pop(i)
if len(ret1) > 0:
return np.concatenate(ret1)
else:
return np.empty((0, 5))