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.
| def deep_sort.kalman_filter.KalmanFilter.gating_distance |
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self, |
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mean, |
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covariance, |
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measurements, |
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only_position = False |
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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
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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 189 of file kalman_filter.py.
| def deep_sort.kalman_filter.KalmanFilter.initiate |
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self, |
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measurement |
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Create track from unassociated measurement.
Parameters
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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.update |
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self, |
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mean, |
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covariance, |
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measurement |
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Run Kalman filter correction step.
Parameters
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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.
Public Attributes inherited from object