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

Extended Object Tracking of Highway Vehicles with Radar and Camera

Extended Object Tracking of Highway Vehicles with Radar and Camera

Track objects whose dimensions span multiple sensor resolution cells.

Open Script
Grid-based Tracking in Urban Environments Using Multiple Lidars

Grid-based Tracking in Urban Environments Using Multiple Lidars

Track moving objects by using multiple lidar sensors and a grid-based tracker.

Open Live Script
Track-to-Track Fusion for Automotive Safety Applications

Track-to-Track Fusion for Automotive Safety Applications

Fuse tracks from two vehicles to provide a more comprehensive estimate of the environment than seen by each vehicle.

Open Live Script
Track-to-Track Fusion for Automotive Safety Applications in Simulink

Track-to-Track Fusion for Automotive Safety Applications in Simulink

Fuse tracks from multiple vehicles in the Simulink environment.

Open Model
Track Vehicles Using Lidar: From Point Cloud to Track List

Track Vehicles Using Lidar: From Point Cloud to Track List

Track vehicles from lidar data using a joint probabilistic data association (JPDA) tracker and an interacting multiple model (IMM) approach.

Open Script
Track Vehicles Using Lidar Data in Simulink

Track Vehicles Using Lidar Data in Simulink

Track vehicles from lidar data using a JPDA tracker and IMM approach in the Simulink environment.

Open Model
Track Multiple Lane Boundaries with a Global Nearest Neighbor Tracker

Track Multiple Lane Boundaries with a Global Nearest Neighbor Tracker

Design and test a multiple-lane tracking algorithm.

Open Live Script
Track-Level Fusion of Radar and Lidar Data

Track-Level Fusion of Radar and Lidar Data

Fuse tracks obtained by radar and lidar sensor measurements.

Open Script
Track-Level Fusion of Radar and Lidar Data in Simulink

Track-Level Fusion of Radar and Lidar Data in Simulink

Fuse tracks obtained by radar and lidar sensor measurements in Simulink.

Open Script
Forward Vehicle Sensor Fusion

Forward Vehicle Sensor Fusion

Simulate sensor fusion and tracking in a 3D simulation environment for automated driving applications. The example also shows how to use performance metrics to evaluate the performance of a tracker in an open-loop environment.

Open Example
Surround Vehicle Sensor Fusion

Surround Vehicle Sensor Fusion

Implement a synthetic data simulation to detect vehicles using multiple vision and radar sensors, and generate fused tracks for surround view analysis in Simulink® with Automated Driving Toolbox™. It also shows how to use quantitative analysis tools in Sensor Fusion and Tracking Toolbox™ for assessing the performance of a tracker.

Open Script
Highway Vehicle Tracking with Multipath Radar Reflections

Highway Vehicle Tracking with Multipath Radar Reflections

Assess and mitigate the impact of multipath radar reflections when tracking highway vehicles using an extended object tracker.

Open Live Script

Automated Driving Toolbox Documentation

Support

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