Inheritance diagram for deep_sort.kalman_filter.KalmanFilter:
Public Member Functions | |
| def | __init__ |
| def | gating_distance |
| def | initiate |
| def | predict |
| def | project |
| def | update |
Private Attributes | |
| _motion_mat | |
| _std_weight_position | |
| _std_weight_velocity | |
| _update_mat | |
Detailed Description
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).
Definition at line 23 of file kalman_filter.py.
Constructor & Destructor Documentation
| def deep_sort.kalman_filter.KalmanFilter.__init__ | ( | self | ) |
Definition at line 40 of file kalman_filter.py.
Member Function Documentation
| def deep_sort.kalman_filter.KalmanFilter.gating_distance | ( | self, | |
| mean, | |||
| covariance, | |||
| measurements, | |||
only_position = False |
|||
| ) |
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]`.
Definition at line 188 of file kalman_filter.py.
| def deep_sort.kalman_filter.KalmanFilter.initiate | ( | self, | |
| 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.
Definition at line 55 of file kalman_filter.py.
| def deep_sort.kalman_filter.KalmanFilter.predict | ( | self, | |
| 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.
Definition at line 88 of file kalman_filter.py.
| def deep_sort.kalman_filter.KalmanFilter.project | ( | self, | |
| 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.
Definition at line 125 of file kalman_filter.py.
| def deep_sort.kalman_filter.KalmanFilter.update | ( | self, | |
| 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.
Definition at line 154 of file kalman_filter.py.
Member Data Documentation
Definition at line 40 of file kalman_filter.py.
Definition at line 40 of file kalman_filter.py.
Definition at line 40 of file kalman_filter.py.
Definition at line 40 of file kalman_filter.py.
The documentation for this class was generated from the following file: