Reference for ultralytics/trackers/utils/kalman_filter.py
Note
Full source code for this file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/trackers/utils/kalman_filter.py. Help us fix any issues you see by submitting a Pull Request 🛠️. Thank you 🙏!
ultralytics.trackers.utils.kalman_filter.KalmanFilterXYAH
For bytetrack. A simple Kalman filter for tracking bounding boxes in image space.
The 8-dimensional state space (x, y, a, h, vx, vy, va, vh) contains the bounding box center position (x, y), aspect ratio a, height h, and their respective velocities.
Object motion follows a constant velocity model. The bounding box location (x, y, a, h) is taken as direct observation of the state space (linear observation model).
Source code in ultralytics/trackers/utils/kalman_filter.py
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 |
|
__init__()
Initialize Kalman filter model matrices with motion and observation uncertainty weights.
Source code in ultralytics/trackers/utils/kalman_filter.py
gating_distance(mean, covariance, measurements, only_position=False, metric='maha')
Compute gating distance between state distribution and measurements. A suitable distance threshold can be
obtained from chi2inv95
. If only_position
is False, the chi-square distribution has 4 degrees of
freedom, otherwise 2.
Parameters
mean : ndarray Mean vector over the state distribution (8 dimensional). covariance : ndarray Covariance of the state distribution (8x8 dimensional). measurements : ndarray An Nx4 dimensional matrix of N measurements, each in format (x, y, a, h) where (x, y) is the bounding box center position, a the aspect ratio, and h the height. only_position : Optional[bool] If True, distance computation is done with respect to the bounding box center position only.
Returns
ndarray
Returns an array of length N, where the i-th element contains the squared Mahalanobis distance between
(mean, covariance) and measurements[i]
.
Source code in ultralytics/trackers/utils/kalman_filter.py
initiate(measurement)
Create track from unassociated measurement.
Parameters
measurement : ndarray Bounding box coordinates (x, y, a, h) with center position (x, y), aspect ratio a, and height h.
Returns
(ndarray, ndarray) Returns the mean vector (8 dimensional) and covariance matrix (8x8 dimensional) of the new track. Unobserved velocities are initialized to 0 mean.
Source code in ultralytics/trackers/utils/kalman_filter.py
multi_predict(mean, covariance)
Run Kalman filter prediction step (Vectorized version).
Parameters
mean : ndarray The Nx8 dimensional mean matrix of the object states at the previous time step. covariance : ndarray The Nx8x8 dimensional covariance matrix of the object states at the previous time step.
Returns
(ndarray, ndarray) Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are initialized to 0 mean.
Source code in ultralytics/trackers/utils/kalman_filter.py
predict(mean, covariance)
Run Kalman filter prediction step.
Parameters
mean : ndarray The 8 dimensional mean vector of the object state at the previous time step. covariance : ndarray The 8x8 dimensional covariance matrix of the object state at the previous time step.
Returns
(ndarray, ndarray) Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are initialized to 0 mean.
Source code in ultralytics/trackers/utils/kalman_filter.py
project(mean, covariance)
Project state distribution to measurement space.
Parameters
mean : ndarray The state's mean vector (8 dimensional array). covariance : ndarray The state's covariance matrix (8x8 dimensional).
Returns
(ndarray, ndarray) Returns the projected mean and covariance matrix of the given state estimate.
Source code in ultralytics/trackers/utils/kalman_filter.py
update(mean, covariance, measurement)
Run Kalman filter correction step.
Parameters
mean : ndarray The predicted state's mean vector (8 dimensional). covariance : ndarray The state's covariance matrix (8x8 dimensional). measurement : ndarray The 4 dimensional measurement vector (x, y, a, h), where (x, y) is the center position, a the aspect ratio, and h the height of the bounding box.
Returns
(ndarray, ndarray) Returns the measurement-corrected state distribution.
Source code in ultralytics/trackers/utils/kalman_filter.py
ultralytics.trackers.utils.kalman_filter.KalmanFilterXYWH
Bases: KalmanFilterXYAH
For BoT-SORT. A simple Kalman filter for tracking bounding boxes in image space.
The 8-dimensional state space (x, y, w, h, vx, vy, vw, vh) contains the bounding box center position (x, y), width w, height h, and their respective velocities.
Object motion follows a constant velocity model. The bounding box location (x, y, w, h) is taken as direct observation of the state space (linear observation model).
Source code in ultralytics/trackers/utils/kalman_filter.py
222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 |
|
initiate(measurement)
Create track from unassociated measurement.
Parameters
measurement : ndarray Bounding box coordinates (x, y, w, h) with center position (x, y), width w, and height h.
Returns
(ndarray, ndarray) Returns the mean vector (8 dimensional) and covariance matrix (8x8 dimensional) of the new track. Unobserved velocities are initialized to 0 mean.
Source code in ultralytics/trackers/utils/kalman_filter.py
multi_predict(mean, covariance)
Run Kalman filter prediction step (Vectorized version).
Parameters
mean : ndarray The Nx8 dimensional mean matrix of the object states at the previous time step. covariance : ndarray The Nx8x8 dimensional covariance matrix of the object states at the previous time step.
Returns
(ndarray, ndarray) Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are initialized to 0 mean.
Source code in ultralytics/trackers/utils/kalman_filter.py
predict(mean, covariance)
Run Kalman filter prediction step.
Parameters
mean : ndarray The 8 dimensional mean vector of the object state at the previous time step. covariance : ndarray The 8x8 dimensional covariance matrix of the object state at the previous time step.
Returns
(ndarray, ndarray) Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are initialized to 0 mean.
Source code in ultralytics/trackers/utils/kalman_filter.py
project(mean, covariance)
Project state distribution to measurement space.
Parameters
mean : ndarray The state's mean vector (8 dimensional array). covariance : ndarray The state's covariance matrix (8x8 dimensional).
Returns
(ndarray, ndarray) Returns the projected mean and covariance matrix of the given state estimate.
Source code in ultralytics/trackers/utils/kalman_filter.py
update(mean, covariance, measurement)
Run Kalman filter correction step.
Parameters
mean : ndarray The predicted state's mean vector (8 dimensional). covariance : ndarray The state's covariance matrix (8x8 dimensional). measurement : ndarray The 4 dimensional measurement vector (x, y, w, h), where (x, y) is the center position, w the width, and h the height of the bounding box.
Returns
(ndarray, ndarray) Returns the measurement-corrected state distribution.