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Glenn Jocher 1 year ago
parent 6143f590ec
commit d1810125ea
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  1. 166
      ultralytics/trackers/utils/kalman_filter.py

166
ultralytics/trackers/utils/kalman_filter.py
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@ -6,20 +6,13 @@ import scipy.linalg
class KalmanFilterXYAH:
"""
For bytetrack
A simple Kalman filter for tracking bounding boxes in image space.
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).
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).
"""
def __init__(self):
@ -32,14 +25,14 @@ class KalmanFilterXYAH:
self._motion_mat[i, ndim + i] = dt
self._update_mat = np.eye(ndim, 2 * ndim)
# Motion and observation uncertainty are chosen relative to the current
# state estimate. These weights control the amount of uncertainty in
# the model. This is a bit hacky.
# Motion and observation uncertainty are chosen relative to the current state estimate. These weights control
# the amount of uncertainty in the model. This is a bit hacky.
self._std_weight_position = 1. / 20
self._std_weight_velocity = 1. / 160
def initiate(self, measurement):
"""Create track from unassociated measurement.
"""
Create track from unassociated measurement.
Parameters
----------
@ -53,7 +46,6 @@ class KalmanFilterXYAH:
Returns the mean vector (8 dimensional) and covariance matrix (8x8
dimensional) of the new track. Unobserved velocities are initialized
to 0 mean.
"""
mean_pos = measurement
mean_vel = np.zeros_like(mean_pos)
@ -67,23 +59,21 @@ class KalmanFilterXYAH:
return mean, covariance
def predict(self, mean, covariance):
"""Run Kalman filter prediction step.
"""
Run Kalman filter prediction step.
Parameters
----------
mean : ndarray
The 8 dimensional mean vector of the object state at the previous
time step.
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.
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.
Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are
initialized to 0 mean.
"""
std_pos = [
self._std_weight_position * mean[3], self._std_weight_position * mean[3], 1e-2,
@ -100,7 +90,8 @@ class KalmanFilterXYAH:
return mean, covariance
def project(self, mean, covariance):
"""Project state distribution to measurement space.
"""
Project state distribution to measurement space.
Parameters
----------
@ -112,9 +103,7 @@ class KalmanFilterXYAH:
Returns
-------
(ndarray, ndarray)
Returns the projected mean and covariance matrix of the given state
estimate.
Returns the projected mean and covariance matrix of the given state estimate.
"""
std = [
self._std_weight_position * mean[3], self._std_weight_position * mean[3], 1e-1,
@ -126,20 +115,21 @@ class KalmanFilterXYAH:
return mean, covariance + innovation_cov
def multi_predict(self, mean, covariance):
"""Run Kalman filter prediction step (Vectorized version).
"""
Run Kalman filter prediction step (Vectorized version).
Parameters
----------
mean : ndarray
The Nx8 dimensional mean matrix of the object states at the previous
time step.
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.
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.
Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are
initialized to 0 mean.
"""
std_pos = [
self._std_weight_position * mean[:, 3], self._std_weight_position * mean[:, 3],
@ -159,7 +149,8 @@ class KalmanFilterXYAH:
return mean, covariance
def update(self, mean, covariance, measurement):
"""Run Kalman filter correction step.
"""
Run Kalman filter correction step.
Parameters
----------
@ -168,15 +159,13 @@ class KalmanFilterXYAH:
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.
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.
"""
projected_mean, projected_cov = self.project(mean, covariance)
@ -191,10 +180,11 @@ class KalmanFilterXYAH:
return new_mean, new_covariance
def gating_distance(self, 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
"""
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
@ -202,18 +192,16 @@ class KalmanFilterXYAH:
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.
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.
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]`.
Returns an array of length N, where the i-th element contains the squared Mahalanobis distance between
(mean, covariance) and `measurements[i]`.
"""
mean, covariance = self.project(mean, covariance)
if only_position:
@ -233,38 +221,29 @@ class KalmanFilterXYAH:
class KalmanFilterXYWH(KalmanFilterXYAH):
"""
For BoT-SORT
A simple Kalman filter for tracking bounding boxes in image space.
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).
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).
"""
def initiate(self, measurement):
"""Create track from unassociated 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.
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.
Returns the mean vector (8 dimensional) and covariance matrix (8x8 dimensional) of the new track.
Unobserved velocities are initialized to 0 mean.
"""
mean_pos = measurement
mean_vel = np.zeros_like(mean_pos)
@ -279,23 +258,21 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
return mean, covariance
def predict(self, mean, covariance):
"""Run Kalman filter prediction step.
"""
Run Kalman filter prediction step.
Parameters
----------
mean : ndarray
The 8 dimensional mean vector of the object state at the previous
time step.
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.
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.
Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are
initialized to 0 mean.
"""
std_pos = [
self._std_weight_position * mean[2], self._std_weight_position * mean[3],
@ -311,7 +288,8 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
return mean, covariance
def project(self, mean, covariance):
"""Project state distribution to measurement space.
"""
Project state distribution to measurement space.
Parameters
----------
@ -323,9 +301,7 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
Returns
-------
(ndarray, ndarray)
Returns the projected mean and covariance matrix of the given state
estimate.
Returns the projected mean and covariance matrix of the given state estimate.
"""
std = [
self._std_weight_position * mean[2], self._std_weight_position * mean[3],
@ -337,20 +313,21 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
return mean, covariance + innovation_cov
def multi_predict(self, mean, covariance):
"""Run Kalman filter prediction step (Vectorized version).
"""
Run Kalman filter prediction step (Vectorized version).
Parameters
----------
mean : ndarray
The Nx8 dimensional mean matrix of the object states at the previous
time step.
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.
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.
Returns the mean vector and covariance matrix of the predicted state. Unobserved velocities are
initialized to 0 mean.
"""
std_pos = [
self._std_weight_position * mean[:, 2], self._std_weight_position * mean[:, 3],
@ -370,7 +347,8 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
return mean, covariance
def update(self, mean, covariance, measurement):
"""Run Kalman filter correction step.
"""
Run Kalman filter correction step.
Parameters
----------
@ -379,14 +357,12 @@ class KalmanFilterXYWH(KalmanFilterXYAH):
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.
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.
"""
return super().update(mean, covariance, measurement)

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