# Initiator & Deleter
deleter = CovarianceBasedDeleter(covar_trace_thresh=1E3)
initiator = MultiMeasurementInitiator(
GaussianState(np.array([[0], [0], [0], [0]]), np.diag([0, 100, 0, 1000])),
measurement_model=measurement_model,
deleter=deleter,
data_associator=data_associator,
updater=updater,
min_points=3,
)
# Tracker
tracker = MultiTargetTracker(
initiator=initiator,
deleter=deleter,
detector=detection_sim,
data_associator=data_associator,
updater=updater,
)
# %%
# Create Metric Generators
# ------------------------
# Here we are going to create a variety of metrics. First up is some "Basic Metrics", that simply
# computes the number of tracks, number to targets and the ratio of tracks to targets. Basic but
# useful information, that requires no additional properties.
from stonesoup.metricgenerator.basicmetrics import BasicMetrics
basic_generator = BasicMetrics()
# %%
from stonesoup.hypothesiser.distance import DistanceHypothesiser
from stonesoup.measures import Mahalanobis
hypothesiser = DistanceHypothesiser(predictor,
updater,
measure=Mahalanobis(),
missed_distance=3)
from stonesoup.dataassociator.neighbour import NearestNeighbour
data_associator = NearestNeighbour(hypothesiser)
deleter = UpdateTimeStepsDeleter(time_steps_since_update=2)
# Create a Kalman multi-target tracker
kalman_tracker = MultiTargetTracker(initiator=initiator,
deleter=deleter,
detector=sim,
data_associator=data_associator,
updater=updater)
# %%
# The final step is to iterate our tracker over the simulation:
kalman_tracks = {} # Store for plotting later
groundtruth_paths = {} # Store for plotting later
detections = [] # Store for plotting later
for time, ctracks in kalman_tracker.tracks_gen():
for track in ctracks:
loc = (track.state_vector[0], track.state_vector[2])
if track not in kalman_tracks:
kalman_tracks[track] = []
kalman_tracks[track].append(loc)
# been updated for a defined time period, in this case 10 minutes.
from stonesoup.deleter.time import UpdateTimeDeleter
deleter = UpdateTimeDeleter(datetime.timedelta(minutes=10))
# %%
# Building and running the tracker
# --------------------------------
# With all the individual components specified we can now build our tracker. This is as simple as
# passing in the components.
from stonesoup.tracker.simple import MultiTargetTracker
tracker = MultiTargetTracker(
initiator=initiator,
deleter=deleter,
detector=detector,
data_associator=data_associator,
updater=updater,
)
# %%
# Our tracker is built and our detections are ready to be read in from the CSV file, now we set the
# tracker to work. This is done by initiating a loop to generate tracks at each time interval.
# We'll keep a record of all tracks generated over time in a :class:`set` called `tracks`; as a
# :class:`set` we can simply update this with `current_tracks` at each timestep, not worrying about
# duplicates.
tracks = set()
for step, (time, current_tracks) in enumerate(tracker.tracks_gen(), 1):
tracks.update(current_tracks)
if not step % 10:
print("Step: {} Time: {}".format(step, time))
data_associator=data_associator,
updater=updater,
min_points=min_detections)
# %%
# Run the Tracker
# ---------------
# With the components created, the multi-target tracker component is created, constructed from
# the components specified above. This is logically the same as tracking code in the previous
# tutorial section :ref:`auto_tutorials/09_Initiators_&_Deleters:Running the Tracker`
from stonesoup.tracker.simple import MultiTargetTracker
tracker = MultiTargetTracker(
initiator=initiator,
deleter=deleter,
detector=detection_sim,
data_associator=data_associator,
updater=updater,
)
# %%
# In the case of using (J)PDA like in :ref:`auto_tutorials/07_PDATutorial:Run the PDA Filter`
# and :ref:`auto_tutorials/08_JPDATutorial:Running the JPDA filter`, then the
# :class:`~.MultiTargetMixtureTracker` would be used instead on the
# :class:`~.MultiTargetTracker` used above.
#
# Plot the outputs
# ^^^^^^^^^^^^^^^^
# We plot the output using a Stone Soup :class:`MetricGenerator` which does plots (in this instance
# :class:`TwoDPlotter`. This will produce plots equivalent to that seen in previous tutorials.
groundtruth = set()
