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Tracking and Sensor Fusion

Object tracking and multisensor fusion, bird’s-eye plot of detections and object tracks

You can create a multi-object tracker to fuse information from radar and video camera sensors. The tracker uses Kalman filters that let you estimate the state of motion of a detected object. Use the sensor measurements made on a detected object to continuously solve for the position and velocity of that object. To track moving objects, you can use constant-velocity or constant-acceleration motion models, or you can define your own models.

Functions

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multiObjectTrackerTrack objects using GNN assignment
objectDetectionReport for single object detection
getTrackPositionsReturns updated track positions and position covariance matrix
getTrackVelocitiesObtain updated track velocities and velocity covariance matrix
objectTrackSingle object track report
trackHistoryLogicConfirm and delete tracks based on recent track history

Alpha-Beta Filter

trackingABFAlpha-beta filter for object tracking
initcaabfCreate constant acceleration alpha-beta tracking filter from detection report
initcvabfCreate constant velocity tracking alpha-beta filter from detection report

Linear Kalman Filter

trackingKFLinear Kalman filter for object tracking
initcakfCreate constant-acceleration linear Kalman filter from detection report
initcvkfCreate constant-velocity linear Kalman filter from detection report

Extended Kalman Filter

trackingEKFExtended Kalman filter for object tracking
initcaekfCreate constant-acceleration extended Kalman filter from detection report
initctekfCreate constant turn-rate extended Kalman filter from detection report
initcvekfCreate constant-velocity extended Kalman filter from detection report

Unscented Kalman Filter

trackingUKFUnscented Kalman filter for object tracking
initcaukfCreate constant-acceleration unscented Kalman filter from detection report
initctukfCreate constant turn-rate unscented Kalman filter from detection report
initcvukfCreate constant-velocity unscented Kalman filter from detection report

Constant Velocity

constvelConstant velocity state update
constveljacJacobian for constant-velocity motion
cvmeasMeasurement function for constant velocity motion
cvmeasjacJacobian of measurement function for constant velocity motion

Constant Acceleration

constaccConstant-acceleration motion model
constaccjacJacobian for constant-acceleration motion
cameasMeasurement function for constant-acceleration motion
cameasjacJacobian of measurement function for constant-acceleration motion

Constant Turn-Rate

constturnConstant turn-rate motion model
constturnjacJacobian for constant turn-rate motion
ctmeasMeasurement function for constant turn-rate motion
ctmeasjacJacobian of measurement function for constant turn-rate motion

Blocks

Multi-Object TrackerCreate and manage tracks of multiple objects

Topics

Multi-Object Tracking

Multiple Object Tracking Tutorial

Perform automatic detection and motion-based tracking of moving objects in a video by using a multi-object tracker.

Linear Kalman Filters

Estimate and predict object motion using a Linear Kalman filter.

Extended Kalman Filters

Estimate and predict object motion using an extended Kalman filter.

Sensor Fusion with Synthetic Data

Sensor Fusion Using Synthetic Radar and Vision Data

Generate a scenario, simulate sensor detections, and use sensor fusion to track simulated vehicles.

Sensor Fusion Using Synthetic Radar and Vision Data in Simulink

Implement a synthetic data simulation for tracking and sensor fusion in Simulink® with Automated Driving Toolbox™.

Code Generation

Code Generation for Tracking and Sensor Fusion

Generate C code for a MATLAB® function that processes data recorded from a test vehicle and tracks the objects around it.

Generate Code for a Track Fuser with Heterogeneous Source Tracks

Generate code for a track-level fusion algorithm where tracks originate from heterogeneous sources with different state definitions.

Featured Examples