Spaces:
Runtime error
Runtime error
File size: 5,065 Bytes
7734d5b |
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 |
# vim: expandtab:ts=4:sw=4
class TrackState:
"""
Enumeration type for the single target track state. Newly created tracks are
classified as `tentative` until enough evidence has been collected. Then,
the track state is changed to `confirmed`. Tracks that are no longer alive
are classified as `deleted` to mark them for removal from the set of active
tracks.
"""
Tentative = 1
Confirmed = 2
Deleted = 3
class Track:
"""
A single target track with state space `(x, y, a, h)` and associated
velocities, where `(x, y)` is the center of the bounding box, `a` is the
aspect ratio and `h` is the height.
Parameters
----------
mean : ndarray
Mean vector of the initial state distribution.
covariance : ndarray
Covariance matrix of the initial state distribution.
track_id : int
A unique track identifier.
n_init : int
Number of consecutive detections before the track is confirmed. The
track state is set to `Deleted` if a miss occurs within the first
`n_init` frames.
max_age : int
The maximum number of consecutive misses before the track state is
set to `Deleted`.
feature : Optional[ndarray]
Feature vector of the detection this track originates from. If not None,
this feature is added to the `features` cache.
Attributes
----------
mean : ndarray
Mean vector of the initial state distribution.
covariance : ndarray
Covariance matrix of the initial state distribution.
track_id : int
A unique track identifier.
hits : int
Total number of measurement updates.
age : int
Total number of frames since first occurance.
time_since_update : int
Total number of frames since last measurement update.
state : TrackState
The current track state.
features : List[ndarray]
A cache of features. On each measurement update, the associated feature
vector is added to this list.
"""
def __init__(self, mean, covariance, track_id, class_id, n_init, max_age,
feature=None):
self.mean = mean
self.covariance = covariance
self.track_id = track_id
self.class_id = class_id
self.hits = 1
self.age = 1
self.time_since_update = 0
self.state = TrackState.Tentative
self.features = []
if feature is not None:
self.features.append(feature)
self._n_init = n_init
self._max_age = max_age
def to_tlwh(self):
"""Get current position in bounding box format `(top left x, top left y,
width, height)`.
Returns
-------
ndarray
The bounding box.
"""
ret = self.mean[:4].copy()
ret[2] *= ret[3]
ret[:2] -= ret[2:] / 2
return ret
def to_tlbr(self):
"""Get current position in bounding box format `(min x, miny, max x,
max y)`.
Returns
-------
ndarray
The bounding box.
"""
ret = self.to_tlwh()
ret[2:] = ret[:2] + ret[2:]
return ret
def increment_age(self):
self.age += 1
self.time_since_update += 1
def predict(self, kf):
"""Propagate the state distribution to the current time step using a
Kalman filter prediction step.
Parameters
----------
kf : kalman_filter.KalmanFilter
The Kalman filter.
"""
self.mean, self.covariance = kf.predict(self.mean, self.covariance)
self.increment_age()
def update(self, kf, detection):
"""Perform Kalman filter measurement update step and update the feature
cache.
Parameters
----------
kf : kalman_filter.KalmanFilter
The Kalman filter.
detection : Detection
The associated detection.
"""
self.mean, self.covariance = kf.update(
self.mean, self.covariance, detection.to_xyah())
self.features.append(detection.feature)
self.hits += 1
self.time_since_update = 0
if self.state == TrackState.Tentative and self.hits >= self._n_init:
self.state = TrackState.Confirmed
def mark_missed(self):
"""Mark this track as missed (no association at the current time step).
"""
if self.state == TrackState.Tentative:
self.state = TrackState.Deleted
elif self.time_since_update > self._max_age:
self.state = TrackState.Deleted
def is_tentative(self):
"""Returns True if this track is tentative (unconfirmed).
"""
return self.state == TrackState.Tentative
def is_confirmed(self):
"""Returns True if this track is confirmed."""
return self.state == TrackState.Confirmed
def is_deleted(self):
"""Returns True if this track is dead and should be deleted."""
return self.state == TrackState.Deleted |