Multi-Object Trackers
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
Blocks
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
objectDetectionobjects. - 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.
- Introduction to PHD Filter
This example introduces the principles behind the probability hypothesis density (PHD) filter and how it can be used to estimate the number and states of multiple objects in a scene. (Since R2023b)
- Generate Code with Strict Single-Precision and Non-Dynamic Memory Allocation
Introduce functions, objects, and blocks that support strict single-precision and non-dynamic memory allocation code generation in Sensor Fusion and Tracking Toolbox™.
Featured Examples
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.
Introduction to Class Fusion and Classification-Aided Tracking
Perform class fusion using multi-object tracker.
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.
Define and Test Tracking Architectures for System-of-Systems
Define the tracking architecture of a system-of-systems that includes multiple detection-level multi-object trackers and track-level fusion algorithms. You can use the tracking architectures to compare different tracking system designs and find the best solution for your system.
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.
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.
Analyze Track and Detection Association Using Analysis Info
Use tracker analysis information to analyze detection and track association in tracking process.
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.
Handle Out-of-Sequence Measurements in Multisensor Tracking Systems
Handle out-of-sequence measurements (OOSM) in a multisensor tracking system. The example compares tracking results when OOSM are present using various handling techniques. For more information about OOSM handling techniques see Handle Out-of-Sequence Measurements with Filter Retrodiction example.
Introduction to JIPDA Smoothing
Introduce joint integrated probabilistic data association multi-object smoothing algorithm and its applications.
Understand and Analyze JIPDA Smoother Algorithm
Illustrate workflow of joint integrated probabilistic data association multi-object smoother algorithm.
Define and Test Tracking Architectures for System-of-Systems in Simulink
Define architectures for a tracking system-of-systems in MATLAB and export them to a Simulink model. You compare various tracking system designs that includes multiple detection-level multi-object trackers and track fusers in Simulink. You use Simulink Variant systems to realize different architecture solutions for your system. This example closely follows the Define and Test Tracking Architectures for System-of-Systems MATLAB example.
Export trackingArchitecture to Simulink
Define a trackingArchitecture object and export it to 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.
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.
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.
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.
