다중 객체 추적기

다중 센서 다중 객체 추적기, 데이터 연결, 트랙 융합

다양한 센서의 정보를 융합하는 다중 객체 추적기를 만들 수 있습니다. 추적된 객체에 대해 단일 가설을 관리하려면 trackerGNN을 사용합니다. 추적된 객체에 대해 여러 가설을 관리하려면 trackerTOMHT를 사용합니다. 추적된 객체에 여러 가능한 탐지를 할당하려면 trackerJPDA를 사용합니다. PHD(확률 가설 밀도) 함수를 사용하여 추적된 객체를 나타내려면 trackerPHD를 사용합니다. 그리드 기반 점유 증거(occupancy evidence) 접근법을 사용하여 객체를 추적하려면 trackerGridRFS를 사용합니다. 추적 센서나 추적기가 생성한 트랙을 융합하고 분산화된 추적 시스템을 설계하려면 trackFuser를 사용합니다.

함수

모두 확장

할당

`assignauction`	Assignment using auction global nearest neighbor
`assignjv`	Jonker-Volgenant global nearest neighbor assignment algorithm
`assignkbest`	Assignment using k-best global nearest neighbor
`assignkbestsd`	K-best S-D solution that minimizes total cost of assignment
`assignmunkres`	Munkres global nearest neighbor assignment algorithm
`assignsd`	S-D assignment using Lagrangian relaxation
`assignTOMHT`	Track-oriented multi-hypotheses tracking assignment
`jpdaEvents`	Feasible joint events for trackerJPDA
`partitionDetections`	Partition detections based on distance
`mergeDetections`	Merge detections into clustered detections (R2021b 이후)

추적기

`trackerGNN`	Multi-sensor, multi-object tracker using GNN assignment
`trackerJPDA`	Joint probabilistic data association tracker
`trackerTOMHT`	Multi-hypothesis, multi-sensor, multi-object tracker
`trackerPHD`	Multi-sensor, multi-object PHD tracker
`trackerGridRFS`	Grid-based multi-object tracker (R2020b 이후)
`smootherJIPDA`	Joint probabilistic data association smoother (R2023a 이후)
`dynamicEvidentialGridMap`	Dynamic grid map output from `trackerGridRFS` (R2021a 이후)
`objectDetection`	Report for single object detection
`objectDetectionDelay`	Simulate out-of-sequence object detections (R2022a 이후)
`getTrackPositions`	Returns updated track positions and position covariance matrix
`getTrackVelocities`	Obtain updated track velocities and velocity covariance matrix
`clusterTrackBranches`	Cluster track-oriented multi-hypothesis history
`compatibleTrackBranches`	Formulate global hypotheses from clusters
`pruneTrackBranches`	Prune track branches with low likelihood
`trackHistoryLogic`	Confirm and delete tracks based on recent track history
`trackScoreLogic`	Confirm and delete tracks based on track score
`trackBranchHistory`	Track-oriented MHT branching and branch history
`trackingSensorConfiguration`	Represent sensor configuration for tracking

트랙과 탐지 융합

`trackFuser`	Single-hypothesis track-to-track fuser
`trackingArchitecture`	Tracking system-of-system architecture (R2021a 이후)
`staticDetectionFuser`	Static fusion of synchronous sensor detections
`objectTrack`	Single object track report
`fusecovint`	Covariance fusion using covariance intersection
`fusecovunion`	Covariance fusion using covariance union
`fusexcov`	Covariance fusion using cross-covariance
`fuserSourceConfiguration`	Configuration of source used with track fuser
`triangulateLOS`	Triangulate multiple line-of-sight detections

블록

모두 확장

추적기

Global Nearest Neighbor Multi Object Tracker	Multi-sensor, multi-object tracker using GNN assignment
Joint Probabilistic Data Association Multi Object Tracker	Joint probabilistic data association tracker
Track-Oriented Multi-Hypothesis Tracker	Track-Oriented Multi-Hypothesis Tracker (R2020a 이후)
Probability Hypothesis Density (PHD) Tracker	Multi-sensor, multi-object PHD tracker (R2021a 이후)
Grid-Based Multi Object Tracker	Grid-based multi-object tracker using random finite set approach (R2021b 이후)
Track-To-Track Fuser	Track-to-Track Fusion (R2021a 이후)
Detection Concatenation	Combine detection reports from different sensors (R2021a 이후)
Track Concatenation	Concatenate tracks (R2021a 이후)

도움말 항목

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.
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. (R2023b 이후)
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™.

추천 예제

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.

스크립트 열기

Introduction to Class Fusion and Classification-Aided Tracking

Perform class fusion using multi-object tracker.

R2022b 이후
라이브 스크립트 열기

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.

스크립트 열기

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.

R2021a 이후
라이브 스크립트 열기

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.

스크립트 열기

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.

라이브 스크립트 열기

Analyze Track and Detection Association Using Analysis Info

Use tracker analysis information to analyze detection and track association in tracking process.

R2022b 이후
라이브 스크립트 열기

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.

라이브 스크립트 열기

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.

R2021b 이후
라이브 스크립트 열기

Introduction to JIPDA Smoothing

Introduce joint integrated probabilistic data association multi-object smoothing algorithm and its applications.

R2023a 이후
라이브 스크립트 열기

Understand and Analyze JIPDA Smoother Algorithm

Illustrate workflow of joint integrated probabilistic data association multi-object smoother algorithm.

R2023a 이후
라이브 스크립트 열기

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.

R2022a 이후
라이브 스크립트 열기

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.

모델 열기

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.

모델 열기

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.

스크립트 열기

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 레이다 데이터 및 라이다 데이터의 트랙에서의 수준 융합 example, in which the state spaces of the tracks generated from lidar and radar sources are different.

라이브 스크립트 열기