OpenCV 卡尔曼滤波器 python
OpenCV Kalman Filter python
谁能给我提供示例代码或卡尔曼滤波器在 python 2.7 和 openCV 2.4.13
中实现的某种示例
我想在视频中实现它来跟踪一个人，但是我没有任何学习参考，也找不到任何 python 示例。
我知道 openCV 中存在卡尔曼滤波器 cv2.KalmanFilter 但我不知道如何使用它。任何指导将不胜感激
下面的kalman.py代码是github中OpenCV 3.2 source中包含的例子。如果需要，将语法改回 2.4 应该很容易。
#!/usr/bin/env python
"""
Tracking of rotating point.
Rotation speed is constant.
Both state and measurements vectors are 1D (a point angle),
Measurement is the real point angle + gaussian noise.
The real and the estimated points are connected with yellow line segment,
the real and the measured points are connected with red line segment.
(if Kalman filter works correctly,
the yellow segment should be shorter than the red one).
Pressing any key (except ESC) will reset the tracking with a different speed.
Pressing ESC will stop the program.
"""
# Python 2/3 compatibility
import sys
PY3 = sys.version_info[0] == 3
if PY3:
long = int
import cv2
from math import cos, sin, sqrt
import numpy as np
if __name__ == "__main__":
img_height = 500
img_width = 500
kalman = cv2.KalmanFilter(2, 1, 0)
code = long(-1)
cv2.namedWindow("Kalman")
while True:
state = 0.1 * np.random.randn(2, 1)
kalman.transitionMatrix = np.array([[1., 1.], [0., 1.]])
kalman.measurementMatrix = 1. * np.ones((1, 2))
kalman.processNoiseCov = 1e-5 * np.eye(2)
kalman.measurementNoiseCov = 1e-1 * np.ones((1, 1))
kalman.errorCovPost = 1. * np.ones((2, 2))
kalman.statePost = 0.1 * np.random.randn(2, 1)
while True:
def calc_point(angle):
return (np.around(img_width/2 + img_width/3*cos(angle), 0).astype(int),
np.around(img_height/2 - img_width/3*sin(angle), 1).astype(int))
state_angle = state[0, 0]
state_pt = calc_point(state_angle)
prediction = kalman.predict()
predict_angle = prediction[0, 0]
predict_pt = calc_point(predict_angle)
measurement = kalman.measurementNoiseCov * np.random.randn(1, 1)
# generate measurement
measurement = np.dot(kalman.measurementMatrix, state) + measurement
measurement_angle = measurement[0, 0]
measurement_pt = calc_point(measurement_angle)
# plot points
def draw_cross(center, color, d):
cv2.line(img,
(center[0] - d, center[1] - d), (center[0] + d, center[1] + d),
color, 1, cv2.LINE_AA, 0)
cv2.line(img,
(center[0] + d, center[1] - d), (center[0] - d, center[1] + d),
color, 1, cv2.LINE_AA, 0)
img = np.zeros((img_height, img_width, 3), np.uint8)
draw_cross(np.int32(state_pt), (255, 255, 255), 3)
draw_cross(np.int32(measurement_pt), (0, 0, 255), 3)
draw_cross(np.int32(predict_pt), (0, 255, 0), 3)
cv2.line(img, state_pt, measurement_pt, (0, 0, 255), 3, cv2.LINE_AA, 0)
cv2.line(img, state_pt, predict_pt, (0, 255, 255), 3, cv2.LINE_AA, 0)
kalman.correct(measurement)
process_noise = sqrt(kalman.processNoiseCov[0,0]) * np.random.randn(2, 1)
state = np.dot(kalman.transitionMatrix, state) + process_noise
cv2.imshow("Kalman", img)
code = cv2.waitKey(100)
if code != -1:
break
if code in [27, ord('q'), ord('Q')]:
break
cv2.destroyWindow("Kalman")
这里是 OpenCV 2.4 Doc 卡尔曼滤波器。希望对您有所帮助。
我知道您特别提到您需要“Python 2.7”代码。不过，如果有人需要，我会提供一些相关信息。
我的频道中关于多目标跟踪的视频：https://www.youtube.com/watch?v=bkn6M4LAoHk
关于卡尔曼滤波和多人跟踪你应该了解的基础知识：
作为传感器的相机：您需要一个合适的检测器（YOLO 等）来为您提供逐帧边界框。
跟踪边界框：
轨道处理由卡尔曼滤波框架完成。八维状态 space 包含边界框中心位置、纵横比、高度及其在图像坐标中的各自速度。标准卡尔曼滤波器与等速运动和线性观测模型一起使用，其中边界坐标作为对象状态的直接观测。[=13=]
Frame-to-Frame association: 如果场景中有三个人怎么办？由于检测器不提供任何关于边界框的标识，因此您需要将当前帧的边界框与之前的边界框进行匹配。我建议你在上面搜索“门控”和“数据关联”关键字。
class KalmanFilter(object):
"""
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).
"""
def __init__(self):
ndim, dt = 4, 1.
# Create Kalman filter model matrices.
self._motion_mat = np.eye(2 * ndim, 2 * ndim)
for i in range(ndim):
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.
self._std_weight_position = 1. / 20
self._std_weight_velocity = 1. / 160
def 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.
"""
mean_pos = measurement
mean_vel = np.zeros_like(mean_pos)
mean = np.r_[mean_pos, mean_vel]
std = [
2 * self._std_weight_position * measurement[3],
2 * self._std_weight_position * measurement[3],
1e-2,
2 * self._std_weight_position * measurement[3],
10 * self._std_weight_velocity * measurement[3],
10 * self._std_weight_velocity * measurement[3],
1e-5,
10 * self._std_weight_velocity * measurement[3]]
covariance = np.diag(np.square(std))
return mean, covariance
def 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.
"""
std_pos = [
self._std_weight_position * mean[3],
self._std_weight_position * mean[3],
1e-2,
self._std_weight_position * mean[3]]
std_vel = [
self._std_weight_velocity * mean[3],
self._std_weight_velocity * mean[3],
1e-5,
self._std_weight_velocity * mean[3]]
motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
mean = np.dot(self._motion_mat, mean)
covariance = np.linalg.multi_dot((
self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
return mean, covariance
def 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.
"""
std = [
self._std_weight_position * mean[3],
self._std_weight_position * mean[3],
1e-1,
self._std_weight_position * mean[3]]
innovation_cov = np.diag(np.square(std))
mean = np.dot(self._update_mat, mean)
covariance = np.linalg.multi_dot((
self._update_mat, covariance, self._update_mat.T))
return mean, covariance + innovation_cov
def 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.
"""
projected_mean, projected_cov = self.project(mean, covariance)
chol_factor, lower = scipy.linalg.cho_factor(
projected_cov, lower=True, check_finite=False)
kalman_gain = scipy.linalg.cho_solve(
(chol_factor, lower), np.dot(covariance, self._update_mat.T).T,
check_finite=False).T
innovation = measurement - projected_mean
new_mean = mean + np.dot(innovation, kalman_gain.T)
new_covariance = covariance - np.linalg.multi_dot((
kalman_gain, projected_cov, kalman_gain.T))
return new_mean, new_covariance
def 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]`.
"""
mean, covariance = self.project(mean, covariance)
if only_position:
mean, covariance = mean[:2], covariance[:2, :2]
measurements = measurements[:, :2]
cholesky_factor = np.linalg.cholesky(covariance)
d = measurements - mean
z = scipy.linalg.solve_triangular(
cholesky_factor, d.T, lower=True, check_finite=False,
overwrite_b=True)
squared_maha = np.sum(z * z, axis=0)
return squared_maha
这是一个基本的多目标跟踪器。
class Tracker:
"""
This is the multi-target tracker.
Parameters
----------
metric : nn_matching.NearestNeighborDistanceMetric
A distance metric for measurement-to-track association.
max_age : int
Maximum number of missed misses before a track is deleted.
n_init : int
Number of consecutive detections before the track is confirmed. The
track state is set to `Deleted` if a miss occurs within the first
`n_init` frames.
Attributes
----------
metric : nn_matching.NearestNeighborDistanceMetric
The distance metric used for measurement to track association.
max_age : int
Maximum number of missed misses before a track is deleted.
n_init : int
Number of frames that a track remains in initialization phase.
kf : kalman_filter.KalmanFilter
A Kalman filter to filter target trajectories in image space.
tracks : List[Track]
The list of active tracks at the current time step.
"""
def __init__(self, metric, max_iou_distance=0.7, max_age=30, n_init=3):
self.metric = metric
self.max_iou_distance = max_iou_distance
self.max_age = max_age
self.n_init = n_init
self.kf = kalman_filter.KalmanFilter()
self.tracks = []
self._next_id = 1
def predict(self):
"""Propagate track state distributions one time step forward.
This function should be called once every time step, before `update`.
"""
for track in self.tracks:
track.predict(self.kf)
def update(self, detections):
"""Perform measurement update and track management.
Parameters
----------
detections : List[deep_sort.detection.Detection]
A list of detections at the current time step.
"""
# Run matching cascade.
matches, unmatched_tracks, unmatched_detections = \
self._match(detections)
# Update track set.
for track_idx, detection_idx in matches:
self.tracks[track_idx].update(
self.kf, detections[detection_idx])
for track_idx in unmatched_tracks:
self.tracks[track_idx].mark_missed()
for detection_idx in unmatched_detections:
self._initiate_track(detections[detection_idx])
self.tracks = [t for t in self.tracks if not t.is_deleted()]
# Update distance metric.
active_targets = [t.track_id for t in self.tracks if t.is_confirmed()]
features, targets = [], []
for track in self.tracks:
if not track.is_confirmed():
continue
features += track.features
targets += [track.track_id for _ in track.features]
track.features = []
self.metric.partial_fit(
np.asarray(features), np.asarray(targets), active_targets)
def _match(self, detections):
def gated_metric(tracks, dets, track_indices, detection_indices):
features = np.array([dets[i].feature for i in detection_indices])
targets = np.array([tracks[i].track_id for i in track_indices])
cost_matrix = self.metric.distance(features, targets)
cost_matrix = linear_assignment.gate_cost_matrix(
self.kf, cost_matrix, tracks, dets, track_indices,
detection_indices)
return cost_matrix
# Split track set into confirmed and unconfirmed tracks.
confirmed_tracks = [
i for i, t in enumerate(self.tracks) if t.is_confirmed()]
unconfirmed_tracks = [
i for i, t in enumerate(self.tracks) if not t.is_confirmed()]
# Associate confirmed tracks using appearance features.
matches_a, unmatched_tracks_a, unmatched_detections = \
linear_assignment.matching_cascade(
gated_metric, self.metric.matching_threshold, self.max_age,
self.tracks, detections, confirmed_tracks)
# Associate remaining tracks together with unconfirmed tracks using IOU.
iou_track_candidates = unconfirmed_tracks + [
k for k in unmatched_tracks_a if
self.tracks[k].time_since_update == 1]
unmatched_tracks_a = [
k for k in unmatched_tracks_a if
self.tracks[k].time_since_update != 1]
matches_b, unmatched_tracks_b, unmatched_detections = \
linear_assignment.min_cost_matching(
iou_matching.iou_cost, self.max_iou_distance, self.tracks,
detections, iou_track_candidates, unmatched_detections)
matches = matches_a + matches_b
unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b))
return matches, unmatched_tracks, unmatched_detections
def _initiate_track(self, detection):
mean, covariance = self.kf.initiate(detection.to_xyah())
self.tracks.append(Track(
mean, covariance, self._next_id, self.n_init, self.max_age,
detection.feature))
self._next_id += 1
谁能给我提供示例代码或卡尔曼滤波器在 python 2.7 和 openCV 2.4.13
中实现的某种示例我想在视频中实现它来跟踪一个人，但是我没有任何学习参考，也找不到任何 python 示例。
我知道 openCV 中存在卡尔曼滤波器 cv2.KalmanFilter 但我不知道如何使用它。任何指导将不胜感激
下面的kalman.py代码是github中OpenCV 3.2 source中包含的例子。如果需要，将语法改回 2.4 应该很容易。
#!/usr/bin/env python
"""
Tracking of rotating point.
Rotation speed is constant.
Both state and measurements vectors are 1D (a point angle),
Measurement is the real point angle + gaussian noise.
The real and the estimated points are connected with yellow line segment,
the real and the measured points are connected with red line segment.
(if Kalman filter works correctly,
the yellow segment should be shorter than the red one).
Pressing any key (except ESC) will reset the tracking with a different speed.
Pressing ESC will stop the program.
"""
# Python 2/3 compatibility
import sys
PY3 = sys.version_info[0] == 3
if PY3:
long = int
import cv2
from math import cos, sin, sqrt
import numpy as np
if __name__ == "__main__":
img_height = 500
img_width = 500
kalman = cv2.KalmanFilter(2, 1, 0)
code = long(-1)
cv2.namedWindow("Kalman")
while True:
state = 0.1 * np.random.randn(2, 1)
kalman.transitionMatrix = np.array([[1., 1.], [0., 1.]])
kalman.measurementMatrix = 1. * np.ones((1, 2))
kalman.processNoiseCov = 1e-5 * np.eye(2)
kalman.measurementNoiseCov = 1e-1 * np.ones((1, 1))
kalman.errorCovPost = 1. * np.ones((2, 2))
kalman.statePost = 0.1 * np.random.randn(2, 1)
while True:
def calc_point(angle):
return (np.around(img_width/2 + img_width/3*cos(angle), 0).astype(int),
np.around(img_height/2 - img_width/3*sin(angle), 1).astype(int))
state_angle = state[0, 0]
state_pt = calc_point(state_angle)
prediction = kalman.predict()
predict_angle = prediction[0, 0]
predict_pt = calc_point(predict_angle)
measurement = kalman.measurementNoiseCov * np.random.randn(1, 1)
# generate measurement
measurement = np.dot(kalman.measurementMatrix, state) + measurement
measurement_angle = measurement[0, 0]
measurement_pt = calc_point(measurement_angle)
# plot points
def draw_cross(center, color, d):
cv2.line(img,
(center[0] - d, center[1] - d), (center[0] + d, center[1] + d),
color, 1, cv2.LINE_AA, 0)
cv2.line(img,
(center[0] + d, center[1] - d), (center[0] - d, center[1] + d),
color, 1, cv2.LINE_AA, 0)
img = np.zeros((img_height, img_width, 3), np.uint8)
draw_cross(np.int32(state_pt), (255, 255, 255), 3)
draw_cross(np.int32(measurement_pt), (0, 0, 255), 3)
draw_cross(np.int32(predict_pt), (0, 255, 0), 3)
cv2.line(img, state_pt, measurement_pt, (0, 0, 255), 3, cv2.LINE_AA, 0)
cv2.line(img, state_pt, predict_pt, (0, 255, 255), 3, cv2.LINE_AA, 0)
kalman.correct(measurement)
process_noise = sqrt(kalman.processNoiseCov[0,0]) * np.random.randn(2, 1)
state = np.dot(kalman.transitionMatrix, state) + process_noise
cv2.imshow("Kalman", img)
code = cv2.waitKey(100)
if code != -1:
break
if code in [27, ord('q'), ord('Q')]:
break
cv2.destroyWindow("Kalman")
这里是 OpenCV 2.4 Doc 卡尔曼滤波器。希望对您有所帮助。
我知道您特别提到您需要“Python 2.7”代码。不过，如果有人需要，我会提供一些相关信息。
我的频道中关于多目标跟踪的视频：https://www.youtube.com/watch?v=bkn6M4LAoHk
关于卡尔曼滤波和多人跟踪你应该了解的基础知识：
作为传感器的相机：您需要一个合适的检测器（YOLO 等）来为您提供逐帧边界框。
跟踪边界框： 轨道处理由卡尔曼滤波框架完成。八维状态 space 包含边界框中心位置、纵横比、高度及其在图像坐标中的各自速度。标准卡尔曼滤波器与等速运动和线性观测模型一起使用，其中边界坐标作为对象状态的直接观测。[=13=]
Frame-to-Frame association: 如果场景中有三个人怎么办？由于检测器不提供任何关于边界框的标识，因此您需要将当前帧的边界框与之前的边界框进行匹配。我建议你在上面搜索“门控”和“数据关联”关键字。
class KalmanFilter(object):
"""
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).
"""
def __init__(self):
ndim, dt = 4, 1.
# Create Kalman filter model matrices.
self._motion_mat = np.eye(2 * ndim, 2 * ndim)
for i in range(ndim):
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.
self._std_weight_position = 1. / 20
self._std_weight_velocity = 1. / 160
def 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.
"""
mean_pos = measurement
mean_vel = np.zeros_like(mean_pos)
mean = np.r_[mean_pos, mean_vel]
std = [
2 * self._std_weight_position * measurement[3],
2 * self._std_weight_position * measurement[3],
1e-2,
2 * self._std_weight_position * measurement[3],
10 * self._std_weight_velocity * measurement[3],
10 * self._std_weight_velocity * measurement[3],
1e-5,
10 * self._std_weight_velocity * measurement[3]]
covariance = np.diag(np.square(std))
return mean, covariance
def 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.
"""
std_pos = [
self._std_weight_position * mean[3],
self._std_weight_position * mean[3],
1e-2,
self._std_weight_position * mean[3]]
std_vel = [
self._std_weight_velocity * mean[3],
self._std_weight_velocity * mean[3],
1e-5,
self._std_weight_velocity * mean[3]]
motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
mean = np.dot(self._motion_mat, mean)
covariance = np.linalg.multi_dot((
self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
return mean, covariance
def 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.
"""
std = [
self._std_weight_position * mean[3],
self._std_weight_position * mean[3],
1e-1,
self._std_weight_position * mean[3]]
innovation_cov = np.diag(np.square(std))
mean = np.dot(self._update_mat, mean)
covariance = np.linalg.multi_dot((
self._update_mat, covariance, self._update_mat.T))
return mean, covariance + innovation_cov
def 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.
"""
projected_mean, projected_cov = self.project(mean, covariance)
chol_factor, lower = scipy.linalg.cho_factor(
projected_cov, lower=True, check_finite=False)
kalman_gain = scipy.linalg.cho_solve(
(chol_factor, lower), np.dot(covariance, self._update_mat.T).T,
check_finite=False).T
innovation = measurement - projected_mean
new_mean = mean + np.dot(innovation, kalman_gain.T)
new_covariance = covariance - np.linalg.multi_dot((
kalman_gain, projected_cov, kalman_gain.T))
return new_mean, new_covariance
def 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]`.
"""
mean, covariance = self.project(mean, covariance)
if only_position:
mean, covariance = mean[:2], covariance[:2, :2]
measurements = measurements[:, :2]
cholesky_factor = np.linalg.cholesky(covariance)
d = measurements - mean
z = scipy.linalg.solve_triangular(
cholesky_factor, d.T, lower=True, check_finite=False,
overwrite_b=True)
squared_maha = np.sum(z * z, axis=0)
return squared_maha
这是一个基本的多目标跟踪器。
class Tracker:
"""
This is the multi-target tracker.
Parameters
----------
metric : nn_matching.NearestNeighborDistanceMetric
A distance metric for measurement-to-track association.
max_age : int
Maximum number of missed misses before a track is deleted.
n_init : int
Number of consecutive detections before the track is confirmed. The
track state is set to `Deleted` if a miss occurs within the first
`n_init` frames.
Attributes
----------
metric : nn_matching.NearestNeighborDistanceMetric
The distance metric used for measurement to track association.
max_age : int
Maximum number of missed misses before a track is deleted.
n_init : int
Number of frames that a track remains in initialization phase.
kf : kalman_filter.KalmanFilter
A Kalman filter to filter target trajectories in image space.
tracks : List[Track]
The list of active tracks at the current time step.
"""
def __init__(self, metric, max_iou_distance=0.7, max_age=30, n_init=3):
self.metric = metric
self.max_iou_distance = max_iou_distance
self.max_age = max_age
self.n_init = n_init
self.kf = kalman_filter.KalmanFilter()
self.tracks = []
self._next_id = 1
def predict(self):
"""Propagate track state distributions one time step forward.
This function should be called once every time step, before `update`.
"""
for track in self.tracks:
track.predict(self.kf)
def update(self, detections):
"""Perform measurement update and track management.
Parameters
----------
detections : List[deep_sort.detection.Detection]
A list of detections at the current time step.
"""
# Run matching cascade.
matches, unmatched_tracks, unmatched_detections = \
self._match(detections)
# Update track set.
for track_idx, detection_idx in matches:
self.tracks[track_idx].update(
self.kf, detections[detection_idx])
for track_idx in unmatched_tracks:
self.tracks[track_idx].mark_missed()
for detection_idx in unmatched_detections:
self._initiate_track(detections[detection_idx])
self.tracks = [t for t in self.tracks if not t.is_deleted()]
# Update distance metric.
active_targets = [t.track_id for t in self.tracks if t.is_confirmed()]
features, targets = [], []
for track in self.tracks:
if not track.is_confirmed():
continue
features += track.features
targets += [track.track_id for _ in track.features]
track.features = []
self.metric.partial_fit(
np.asarray(features), np.asarray(targets), active_targets)
def _match(self, detections):
def gated_metric(tracks, dets, track_indices, detection_indices):
features = np.array([dets[i].feature for i in detection_indices])
targets = np.array([tracks[i].track_id for i in track_indices])
cost_matrix = self.metric.distance(features, targets)
cost_matrix = linear_assignment.gate_cost_matrix(
self.kf, cost_matrix, tracks, dets, track_indices,
detection_indices)
return cost_matrix
# Split track set into confirmed and unconfirmed tracks.
confirmed_tracks = [
i for i, t in enumerate(self.tracks) if t.is_confirmed()]
unconfirmed_tracks = [
i for i, t in enumerate(self.tracks) if not t.is_confirmed()]
# Associate confirmed tracks using appearance features.
matches_a, unmatched_tracks_a, unmatched_detections = \
linear_assignment.matching_cascade(
gated_metric, self.metric.matching_threshold, self.max_age,
self.tracks, detections, confirmed_tracks)
# Associate remaining tracks together with unconfirmed tracks using IOU.
iou_track_candidates = unconfirmed_tracks + [
k for k in unmatched_tracks_a if
self.tracks[k].time_since_update == 1]
unmatched_tracks_a = [
k for k in unmatched_tracks_a if
self.tracks[k].time_since_update != 1]
matches_b, unmatched_tracks_b, unmatched_detections = \
linear_assignment.min_cost_matching(
iou_matching.iou_cost, self.max_iou_distance, self.tracks,
detections, iou_track_candidates, unmatched_detections)
matches = matches_a + matches_b
unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b))
return matches, unmatched_tracks, unmatched_detections
def _initiate_track(self, detection):
mean, covariance = self.kf.initiate(detection.to_xyah())
self.tracks.append(Track(
mean, covariance, self._next_id, self.n_init, self.max_age,
detection.feature))
self._next_id += 1