Tracking and Sensor Fusion
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.
|Track objects using GNN assignment
|Report for single object detection
|Returns updated track positions and position covariance matrix
|Obtain updated track velocities and velocity covariance matrix
|Confirm tentative track (Since R2022b)
|Single object track report (Since R2020a)
|Confirm and delete tracks based on recent track history (Since R2020a)
Kalman Filters for Tracking
|Alpha-beta filter for object tracking (Since R2020a)
|Create constant acceleration alpha-beta tracking filter from detection report (Since R2020a)
|Create constant velocity tracking alpha-beta filter from detection report (Since R2020a)
Linear Kalman Filter
|Linear Kalman filter for object tracking
|Create constant-acceleration linear Kalman filter from detection report
|Create constant-velocity linear Kalman filter from detection report
Extended Kalman Filter
|Extended Kalman filter for object tracking
|Create constant-acceleration extended Kalman filter from detection report
|Create constant turn-rate extended Kalman filter from detection report
|Create constant-velocity extended Kalman filter from detection report
Unscented Kalman Filter
|Constant velocity state update
|Jacobian for constant-velocity motion
|Measurement function for constant velocity motion
|Jacobian of measurement function for constant velocity motion
|Constant-acceleration motion model
|Jacobian for constant-acceleration motion
|Measurement function for constant-acceleration motion
|Jacobian of measurement function for constant-acceleration motion
|Create and manage tracks of multiple objects
- 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.
- Convert Detections to objectDetection Format
These examples show how to convert actual detections in the native format of the sensor into
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 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.