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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.


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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
dynamicEvidentialGridMapDynamic grid map output from trackerGridRFS
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
trackingArchitectureTracking system-of-system architecture
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


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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
Probability Hypothesis Density (PHD) TrackerMulti-sensor, multi-object PHD tracker
Track-To-Track FuserTrack-to-Track Fusion
Detection ConcatenationCombine detection reports from different sensors
Track ConcatenationConcatenate tracks


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