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

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

functions

assignment using auction global nearest neighbor
jonker-volgenant global nearest neighbor assignment algorithm
assignment using k-best global nearest neighbor
k-best s-d solution that minimizes total cost of assignment
munkres global nearest neighbor assignment algorithm
s-d assignment using lagrangian relaxation
track-oriented multi-hypotheses tracking assignment
feasible joint events for trackerjpda
partition detections based on distance
merge detections into clustered detections
multi-sensor, multi-object tracker using gnn assignment
joint probabilistic data association tracker
multi-hypothesis, multi-sensor, multi-object tracker
multi-sensor, multi-object phd tracker
grid-based multi-object tracker
joint probabilistic data association smoother
dynamic grid map output from trackergridrfs
report for single object detection
simulate out-of-sequence object detections
returns updated track positions and position covariance matrix
obtain updated track velocities and velocity covariance matrix
cluster track-oriented multi-hypothesis history
formulate global hypotheses from clusters
prune track branches with low likelihood
confirm and delete tracks based on recent track history
confirm and delete tracks based on track score
track-oriented mht branching and branch history
represent sensor configuration for tracking
single-hypothesis track-to-track fuser
tracking system-of-system architecture
static fusion of synchronous sensor detections
single object track report
covariance fusion using covariance intersection
covariance fusion using covariance union
covariance fusion using cross-covariance
configuration of source used with track fuser
triangulate multiple line-of-sight detections

blocks

multi-sensor, multi-object tracker using gnn assignment
joint probabilistic data association tracker
track-oriented multi-hypothesis tracker
multi-sensor, multi-object phd tracker
grid-based multi-object tracker using random finite set approach
track-to-track fusion
combine detection reports from different sensors
concatenate tracks

topics

  • introduction to multiple target tracking

    introduction to assignment-based multiple target trackers.


  • 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.


  • this example shows how to configure and use the global nearest neighbor (gnn) tracker.


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


  • introduce functions, objects, and blocks that support strict single-precision and non-dynamic memory allocation code generation in sensor fusion and tracking toolbox™.

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