Multi-Object Trackers

Multi-sensor multi-object trackers, data association, and track fusion

You can create multi-object trackers that fuse information from various sensors. Use trackerGNN to maintain a single hypothesis about the tracked objects. Use trackerTOMHT to maintain multiple hypotheses about the tracked objects. Use trackerJPDA to assign multiple probable detections to the tracked objects. Use trackerPHD to represent tracked objects using probability hypothesis density (PHD) function. Use trackerGridRFS to track objects using a grid-based occupancy evidence approach. Use trackFuser to fuse tracks generated by tracking sensors or trackers and architect decentralized tracking systems.

Functions

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assignauctionAssignment using auction global nearest neighbor
assignjvJonker-Volgenant global nearest neighbor assignment algorithm
assignkbestAssignment using k-best global nearest neighbor
assignkbestsdK-best S-D solution that minimizes total cost of assignment
assignmunkresMunkres global nearest neighbor assignment algorithm
assignsdS-D assignment using Lagrangian relaxation
assignTOMHTTrack-oriented multi-hypotheses tracking assignment
jpdaEventsFeasible joint events for trackerJPDA
partitionDetectionsPartition detections based on Mahalanobis distance
trackerGNNMulti-sensor, multi-object tracker using GNN assignment
trackerJPDAJoint probabilistic data association tracker
trackerTOMHTMulti-hypothesis, multi-sensor, multi-object tracker
trackerPHDMulti-sensor, multi-object PHD tracker
trackerGridRFSGrid-based multi-object tracker
objectDetectionReport for single object detection
getTrackPositionsReturns updated track positions and position covariance matrix
getTrackVelocitiesObtain updated track velocities and velocity covariance matrix
clusterTrackBranchesCluster track-oriented multi-hypothesis history
compatibleTrackBranchesFormulate global hypotheses from clusters
pruneTrackBranchesPrune track branches with low likelihood
trackHistoryLogicConfirm and delete tracks based on recent track history
trackScoreLogicConfirm and delete tracks based on track score
trackBranchHistoryTrack-oriented MHT branching and branch history
trackingSensorConfiguration Represent sensor configuration for tracking
trackFuserSingle-hypothesis track-to-track fuser
staticDetectionFuserStatic fusion of synchronous sensor detections
objectTrackSingle object track report
fusecovintCovariance fusion using covariance intersection
fusecovunionCovariance fusion using covariance union
fusexcovCovariance fusion using cross-covariance
fuserSourceConfiguration Configuration of source used with track fuser
triangulateLOSTriangulate multiple line-of-sight detections

Blocks

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Global Nearest Neighbor Multi Object TrackerMulti-sensor, multi-object tracker using GNN assignment
Joint Probabilistic Data Association Multi Object TrackerJoint probabilistic data association tracker
Track-Oriented Multi-Hypothesis TrackerTrack-Oriented Multi-Hypothesis Tracker

Topics

Introduction to Multiple Target Tracking

Introduction to assignment-based multiple target trackers

Introduction to Assignment Methods in Tracking Systems

Introduce 2-D and S-D assignment problems in tracking systems

Introduction to Track-To-Track Fusion

Track-To-Track Fusion Architecture Using Track Fuser

Multiple Extended Object Tracking

Introduction to methods and examples of multiple extended object tracking in the toolbox.

Convert Detections to objectDetection Format

These examples show how to convert actual detections in the native format of the sensor into objectDetection objects.

Introduction to Using the Global Nearest Neighbor Tracker

This example shows how to configure and use the global nearest neighbor (GNN) tracker.

Introduction to Track Logic

This example shows how to define and use confirmation and deletion logic that are based on history or score.

Featured Examples

Tracking Closely Spaced Targets Under Ambiguity

Tracking Closely Spaced Targets Under Ambiguity

Track objects when the association of sensor detections to tracks is ambiguous. In this example, you use a single-hypothesis tracker, a multiple-hypothesis tracker, and a probabilistic data association tracker to compare how the trackers handle this ambiguity. To track, the maneuvering targets better, you estimate the motion of the targets by using various models.

Open Script
Tracking a Flock of Birds

Tracking a Flock of Birds

Track a large number of objects. A large flock of birds is generated and a global nearest neighbor multi-object tracker, trackerGNN, is used to estimate the motion of every bird in the flock.

Open Script
How to Efficiently Track Large Numbers of Objects

How to Efficiently Track Large Numbers of Objects

Use the trackerGNN to track large numbers of targets. Similar techniques can be applied to the trackerJPDA and trackerTOMHT as well.

Open Script
Tuning a Multi-Object Tracker

Tuning a Multi-Object Tracker

Tune and run a tracker to track multiple objects in the scene. The example explains and demonstrates the importance of key properties of the trackers in the Sensor Fusion and Tracking Toolbox.

Open Live Script
Track Multiple Lane Boundaries with a Global Nearest Neighbor Tracker

Track Multiple Lane Boundaries with a Global Nearest Neighbor Tracker

Design and test a multiple lane tracking algorithm. The algorithm is tested in a driving scenario with probabilistic lane detections.

Open Live Script
Track Simulated Vehicles Using GNN and JPDA Trackers in Simulink

Track Simulated Vehicles Using GNN and JPDA Trackers in Simulink

Configure and utilize GNN and JPDA trackers in a simulated highway scenario in Simulink® with Sensor Fusion and Tracking Toolbox™. It closely follows the Sensor Fusion Using Synthetic Radar and Vision Data in Simulink (Automated Driving Toolbox). A main benefit of modeling the system in Simulink is the simplicity of performing "what-if" analysis and choosing a tracker that results in the best performance based on the requirements.

Open Model
Track Closely Spaced Targets Under Ambiguity in Simulink

Track Closely Spaced Targets Under Ambiguity in Simulink

Track objects in Simulink® with Sensor Fusion and Tracking Toolbox™ when the association of sensor detections to tracks is ambiguous. It closely follows the Tracking Closely Spaced Targets Under Ambiguity MATLAB® example.

Open Model
How to Generate C Code for a Tracker

How to Generate C Code for a Tracker

Generate C code for a MATLAB function that processes detections and outputs tracks. The function contains a trackerGNN, but any tracker can be used instead.

Open Script
Generate Code for a Track Fuser with Heterogeneous Source Tracks

Generate Code for a Track Fuser with Heterogeneous Source Tracks

Generate code for a track-level fusion algorithm in a scenario where the tracks originate from heterogeneous sources with different state definitions. This example is based on the Track-Level Fusion of Radar and Lidar Data example, in which the state spaces of the tracks generated from lidar and radar sources are different.

Open Live Script

Sensor Fusion and Tracking Toolbox Documentation

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