import numpy as np
@@ -170,22 +84,14 @@ Source code for motrackers.kalman_tracker
"""
Kalman Filter Implementation.
- Parameters
- ----------
- transition_matrix: numpy.ndarray
- Transition matrix of shape (n, n).
- measurement_matrix: numpy.ndarray
- Measurement matrix of shape (m, n).
- control_matrix: numpy.ndarray
- Control matrix of shape (m, n).
- process_noise_covariance: numpy.ndarray
- Covariance matrix of shape (n, n).
- measurement_noise_covariance: numpy.ndarray
- Covariance matrix of shape (m, m).
- prediction_covariance: numpy.ndarray
- Predicted (a priori) estimate covariance of shape (n, n).
- initial_state: numpy.ndarray
- Initial state of shape (n,).
+ Args:
+ transition_matrix (numpy.ndarray): Transition matrix of shape ``(n, n)``.
+ measurement_matrix (numpy.ndarray): Measurement matrix of shape ``(m, n)``.
+ control_matrix (numpy.ndarray): Control matrix of shape ``(m, n)``.
+ process_noise_covariance (numpy.ndarray): Covariance matrix of shape ``(n, n)``.
+ measurement_noise_covariance (numpy.ndarray): Covariance matrix of shape ``(m, m)``.
+ prediction_covariance (numpy.ndarray): Predicted (a priori) estimate covariance of shape ``(n, n)``.
+ initial_state (numpy.ndarray): Initial state of shape ``(n,)``.
"""
@@ -225,7 +131,7 @@ Source code for motrackers.kalman_tracker
u (float or int or numpy.ndarray): Control input. Default is `0`.
Returns:
- numpy.ndarray : State vector of shape (n,).
+ numpy.ndarray : State vector of shape `(n,)`.
"""
self.x = np.dot(self.transition_matrix, self.x) + np.dot(self.control_matrix, u)
@@ -241,10 +147,7 @@ Source code for motrackers.kalman_tracker
Measurement update of Kalman Filter.
Args:
- z (numpy.ndarray): Measurement vector of the system with shape (m,).
-
- Returns:
-
+ z (numpy.ndarray): Measurement vector of the system with shape ``(m,)``.
"""
y = z - np.dot(self.measurement_matrix, self.x)
@@ -266,6 +169,15 @@ Source code for motrackers.kalman_tracker
def get_process_covariance_matrix(dt):
+ """
+ Generates a process noise covariance matrix for constant acceleration motion.
+
+ Args:
+ dt (float): Timestep.
+
+ Returns:
+ numpy.ndarray: Process covariance matrix of shape `(3, 3)`.
+ """
# a = np.array([
# [0.25 * dt ** 4, 0.5 * dt ** 3, 0.5 * dt ** 2],
# [0.5 * dt ** 3, dt ** 2, dt],
@@ -281,8 +193,17 @@ Source code for motrackers.kalman_tracker
def get_transition_matrix(dt):
- a = np.array([[1., dt, dt * dt * 0.5], [0., 1., dt], [0., 0., 1.]])
- return a
+ """
+ Generate the transition matrix for constant acceleration motion.
+
+ Args:
+ dt (float): Timestep.
+
+ Returns:
+ numpy.ndarray: Transition matrix of shape ``(3, 3)``.
+
+ """
+ return np.array([[1., dt, dt * dt * 0.5], [0., 1., dt], [0., 0., 1.]])
[docs]class KFTrackerConstantAcceleration(KalmanFilter):
@@ -359,12 +280,13 @@
Source code for motrackers.kalman_tracker
[docs]class KFTrackerSORT(KalmanFilter):
"""
+
Kalman filter for ``SORT``.
Args:
-
bbox (numpy.ndarray): Bounding box coordinates as (xmid, ymid, area, aspect_ratio).
+
bbox (numpy.ndarray): Bounding box coordinates as ``(xmid, ymid, area, aspect_ratio)``.
time_step (float or int): Time step.
process_noise_scale (float): Scale (a.k.a covariance) of the process noise.
-
measurement_noise_scale (float): Scale (a.k.a covariance) of the measurement noise.
+
measurement_noise_scale (float): Scale (a.k.a. covariance) of the measurement noise.
"""
def __init__(self, bbox, process_noise_scale=1.0, measurement_noise_scale=1.0, time_step=1):
assert bbox.shape[0] == 4, bbox.shape
@@ -489,46 +411,30 @@
Source code for motrackers.kalman_tracker
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-