Optimal Sub-Pattern Assignment (OSPA) Metric

class stonesoup.metricgenerator.ospametric.GOSPAMetric(p: float, c: float, measure: Measure = Euclidean(mapping=None, mapping2=None))[source]

Bases: MetricGenerator

Computes the Generalized Optimal SubPattern Assignment (GOSPA) metric for two sets of Track objects. This implementation of GOSPA is based on the auction algorithm.

The GOSPA metric is calculated at each time step in which a Track object 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. Default Euclidean()

p: float

1<=p<infty, exponent.

c: float

c>0, cutoff distance.

measure: Measure

Distance measure to use. Default Euclidean()

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 StateMutableSequence and State types.

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 (TimeRangeMetric covering 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 c or the distance calculated from Measure.

Parameters:

  • track_states (list of states) –

  • truth_states (list of states) –

  • complete (bool) – Cost matrix will be square, with c present for where there is a mismatch in cardinality

Returns:

cost_matrix – Matrix of distance between each element in each list of states

Return type:

np.ndarray

compute_gospa_metric(measured_states, truth_states)[source]

Computes GOSPA metric between measured and truth states.

Parameters:

  • measured_states (list of State) – list of state objects to be assigned to the truth

  • truth_states (list of State) – list of state objects for the truth points

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))[source]

Bases: GOSPAMetric

Computes the Optimal SubPattern Assignment (OSPA) distance [1] for two sets of Track objects. The OSPA distance is measured between two point patterns.

The OSPA metric is calculated at each time step in which a Track object 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 distance

  • c (float) – Maximum distance for possible association

  • measure (Measure, optional) – Distance measure to use. Default Euclidean()

c: float

Maximum distance for possible association

p: float

norm associated to distance

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:

  • measured_states (list of State) – Created by a filter

  • truth_states (list of State) – Truth states to compare against

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:

TimeRangeMetric

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 State objects.

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:

  • track_states (list of State) –

  • truth_states (list of State) –

Returns:

The OSPA distance

Return type:

SingleTimeMetric