Visualization and Analytics

R2026b

Multi-object theater plots, detection and object tracks, and track metrics

Use theaterPlot to plot ground truth, coverages, detections, and tracks. Get error metrics for tracks using trackErrorMetrics. Analyze and compare performance of multi-object tracking systems using trackAssignmentMetrics, trackOSPAMetric, and trackGOSPAMetric.

Functions

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Visualization

`theaterPlot`	Plot objects, detections, and tracks in Scenario
`trackingGlobeViewer`	Virtual globe for tracking scenario visualization
`addCustomTerrain`	Add custom terrain data (Since R2022a)
`removeCustomTerrain`	Remove custom terrain data (Since R2022a)
`addCustomBasemap`	Add custom basemap (Since R2022a)
`removeCustomBasemap`	Remove custom basemap (Since R2022a)
`poseplot`	3-D pose plot
`PosePatch Properties`	Pose plot appearance and behavior
`timescope`	Display time-domain signals

Analytics

`trackAssignmentMetrics`	Track establishment, maintenance, and deletion metrics
`trackErrorMetrics`	Track error and NEES
`trackOSPAMetric`	Optimal subpattern assignment (OSPA) metric
`trackGOSPAMetric`	Generalized optimal subpattern assignment (GOSPA) metric
`trackCLEARMetrics`	CLEAR multi-object tracking metrics (Since R2023a)
`allanvar`	Allan variance
`magcal`	Magnetometer calibration coefficients

Blocks

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Analytics

	Generalized Optimal Subpattern Assignment Metric	Calculate Generalized Optimal Subpattern Assignment Metric
	Optimal Subpattern Assignment Metric	Calculate Optimal Subpattern Assignment Metric

Topics

Configure Time Scope MATLAB Object
Customize timescope properties and use measurement tools.

Featured Examples

Introduction to Tracking Metrics

While designing a multi-object tracking system, it is essential to devise a method to evaluate its performance against the available ground truth. This ground truth is typically available from a simulation environment or by using techniques like ground-truth extraction using manual or automated labeling on recorded data. Though it is possible to qualitatively evaluate a tracking algorithm using visualization tools, the approach is usually not scalable. This example introduces different quantitative analysis tools in Sensor Fusion and Tracking Toolbox™ for assessing a tracker's performance. You will also use some common events like false tracks, track swaps etc. encountered while tracking multiple objects to understand the strengths and limitations of these tools.

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Use theaterPlot to Visualize Tracking Scenario

Use the theaterPlot object to visualize various aspects of a tracking scenario.

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Inertial Sensor Noise Analysis Using Allan Variance

Use the Allan variance to determine noise parameters of a MEMS gyroscope. These parameters can be used to model the gyroscope in simulation. The gyroscope measurement is modeled as:

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Magnetometer Calibration

Magnetometers detect magnetic field strength along a sensor's X,Y and Z axes. Accurate magnetic field measurements are essential for sensor fusion and the determination of heading and orientation.

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