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Multi-Object Trackers

Multi-sensor trackers, data association, GNN, and MHT

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.

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
jpadEventsFeasible joint events for trackerJPDA
partitionDetectionsPartition detections based on Mahalanobis distance
trackingSensorConfiguration Represent sensor configuration for tracking
trackerTOMHTMulti-hypothesis, multi-sensor, multi-object tracker
trackerGNNMulti-sensor, multi-object tracker using GNN assignment
trackerJPDAJoint probabilistic data association tracker
trackerPHDMulti-sensor, multi-object PHD tracker
objectDetectionCreate object detection report
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
fusecovintCovariance fusion using covariance intersection
fusecovunionCovariance fusion using covariance union
fusexcovCovariance fusion using cross-covariance
staticDetectionFuserStatic fusion of synchronous sensor detections
triangulateLOSTriangulate multiple line-of-sight detections

Topics

Introduction to Using the Global Nearest Neighbor Tracker

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

Multiple Extended Object Tracking

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

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