Optimal Sub-Pattern Assignment (OSPA) Metric
- class stonesoup.metricgenerator.ospametric.GOSPAMetric(p: float, c: float, measure: Measure = Euclidean(mapping=None, mapping2=None), generator_name: str = 'gospa_generator', tracks_key: str = 'tracks', truths_key: str = 'groundtruth_paths')[source]
Bases:
MetricGeneratorComputes the Generalized Optimal SubPattern Assignment (GOSPA) metric for two sets of
Trackobjects. This implementation of GOSPA is based on the auction algorithm.The GOSPA metric is calculated at each time step in which a
Trackobject is present- Reference:
[1] A. S. Rahmathullah, A. F. García-Fernández, L. Svensson, Generalized optimal sub-pattern assignment metric, 2016, [online] Available: http://arxiv.org/abs/1601.05585.
- Parameters:
p (
float) – 1<=p<infty, exponent.c (
float) – c>0, cutoff distance.measure (
Measure, optional) – Distance measure to use. DefaultEuclidean()generator_name (
str, optional) – Unique identifier to use when accessing generated metrics from MultiManagertracks_key (
str, optional) – Key to access set of tracks added to MetricManagertruths_key (
str, optional) – Key to access set of ground truths added to MetricManager. Or key to access a second set of tracks for track-to-track metric generation
- truths_key: str
Key to access set of ground truths added to MetricManager. Or key to access a second set of tracks for track-to-track metric generation
- compute_metric(manager)[source]
Compute the metric using the data in the metric manager
- Parameters:
manager (
MetricManager) – contains the data to be used to create the metric(s)- Returns:
metric – Containing the metric information. The value of the metric is a list of metrics at each timestamp
- Return type:
list
Metric
- static extract_states(object_with_states)[source]
Extracts a list of states from a list of (or single) objects containing states. This method is defined to handle
StateMutableSequenceandStatetypes.- Parameters:
object_with_states (object containing a list of states) – Method of state extraction depends on the type of the object
- Return type:
list of
State
- compute_over_time(measured_states, truth_states)[source]
Compute the GOSPA metric at every timestep from a list of measured states and truth states.
- Parameters:
measured_states (List of states created by a filter) –
truth_states (List of truth states to compare against) –
- Returns:
metric (
TimeRangeMetriccovering the duration that states)exist for in the parameters. metric.value contains a list of metrics
for the GOSPA metric at each timestamp
- compute_assignments(cost_matrix, max_iter)[source]
Compute assignments using Auction Algorithm.
- Parameters:
cost_matrix (Matrix (size mxn) that denotes the cost of assigning) – mth truth state to each of the n measured states.
max_iter (Maximum number of iterations to perform) –
- Returns:
truth_to_measured (np.ndarray) – Vector of size m, which has indices of the measured objects or ‘-1’ if unassigned.
measured_to_truth (np.ndarray) – Vector of size n, which has indices of the truth objects or ‘-1’ if unassigned.
opt_cost (float) – Scalar value of the optimal assignment
- compute_cost_matrix(track_states, truth_states, complete=False)[source]
Creates the cost matrix between two lists of states
This distance measure here will return distances minimum of either
cor the distance calculated fromMeasure.
- compute_gospa_metric(measured_states, truth_states)[source]
Computes GOSPA metric between measured and truth states.
- Parameters:
- Returns:
gospa_metric (Dictionary containing GOSPA metric for alpha = 2.) – GOSPA metric is divided into four components: 1. distance, 2. localisation, 3. missed, and 4. false. Note that distance = (localisation + missed + false)^1/p
truth_to_measured_assignment (Assignment matrix.)
- class stonesoup.metricgenerator.ospametric.OSPAMetric(p: float, c: float, measure: Measure = Euclidean(mapping=None, mapping2=None), generator_name: str = 'ospa_generator', tracks_key: str = 'tracks', truths_key: str = 'groundtruth_paths')[source]
Bases:
GOSPAMetricComputes the Optimal SubPattern Assignment (OSPA) distance [1] for two sets of
Trackobjects. The OSPA distance is measured between two point patterns.The OSPA metric is calculated at each time step in which a
Trackobject is present- Reference:
[1] A Consistent Metric for Performance Evaluation of Multi-Object Filters, D. Schuhmacher, B. Vo and B. Vo, IEEE Trans. Signal Processing 2008
- Parameters:
p (
float) – Norm associated to distancec (
float) – Maximum distance for possible associationmeasure (
Measure, optional) – Distance measure to use. DefaultEuclidean()generator_name (
str, optional) – Unique identifier to use when accessing generated metrics from MultiManagertracks_key (
str, optional) – Key to access set of tracks added to MetricManagertruths_key (
str, optional) – Key to access set of ground truths added to MetricManager. Or key to access a second set of tracks for track-to-track metric generation
- compute_over_time(measured_states, truth_states)[source]
Compute the OSPA metric at every timestep from a list of measured states and truth states
- Parameters:
- Returns:
Covering the duration that states exist for in the parameters. Metric.value contains a list of metrics for the OSPA distance at each timestamp
- Return type:
- compute_OSPA_distance(track_states, truth_states)[source]
Computes the Optimal SubPattern Assignment (OSPA) metric for a single time step between two point patterns. Each point pattern consisting of a list of
Stateobjects.The function \(\bar{d}_{p}^{(c)}\) is the OSPA metric of order \(p\) with cut-off \(c\). The OSPA metric is defined as:
\[\begin{equation*} \bar{d}_{p}^{(c)}({X},{Y}) := \Biggl( \frac{1}{n} \Bigl({min}_{\substack{ \pi\in\Pi_{n}}} \sum_{i=1}^{m} d^{(c)}(x_{i},y_{\pi(i)})^{p}+ c^{p}(n-m)\Bigr) \Biggr)^{ \frac{1}{p} } \end{equation*}\]- Parameters:
- Returns:
The OSPA distance
- Return type: