マルチオブジェクト トラッカー
さまざまなセンサーからの情報を融合するマルチオブジェクト トラッカーを作成できます。追跡されるオブジェクトに関する単一の仮説を維持するには、trackerGNN を使用します。追跡されるオブジェクトに関する複数の仮説を維持するには、trackerTOMHT を使用します。追跡されるオブジェクトに複数の可能性のある検出を割り当てるには、trackerJPDA を使用します。確率仮説密度 (PHD) 関数を使用して追跡されるオブジェクトを表すには、trackerPHD を使用します。グリッドベースの占有証拠アプローチを使用してオブジェクトを追跡するには、trackerGridRFS を使用します。追跡センサーまたはトラッカーによって生成されたトラックを融合し、分散型追跡システムを作成するには、trackFuser を使用します。
関数
ブロック
トピック
- 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.
- トラック間フュージョンの概要
トラック フューザーを使用したトラック間フュージョン アーキテクチャ。
- 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.objectDetectionis the standard input format for most tracking filters and trackers in the toolbox. The six examples progressively show how to set upobjectDetectionwith varied tracking scenarios. - 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. It introduces the
trackHistoryLogicandtrackScoreLogicobjects and shows how to use them as stand-alone objects and how to use them as part of thetrackerGNN. - 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. The PHD filter is a type of filter derived using a random finite set (RFS) framework for object tracking. (R2023b 以降)
- 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™.
注目の例
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 Simulink で合成レーダーおよびビジョン データを使用したセンサー フュージョン (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.
