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.
|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|
|Single object track report|
|Confirm and delete tracks based on recent track history|
Kalman Filters for Tracking
|Alpha-beta filter for object tracking|
|Create constant acceleration alpha-beta tracking filter from detection report|
|Create constant velocity tracking alpha-beta filter from detection report|
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
|Unscented Kalman filter for object tracking|
|Create constant-acceleration unscented Kalman filter from detection report|
|Create constant turn-rate unscented Kalman filter from detection report|
|Create constant-velocity unscented Kalman filter from detection report|
|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|
|Constant turn-rate motion model|
|Jacobian for constant turn-rate motion|
|Measurement function for constant turn-rate motion|
|Jacobian of measurement function for constant turn-rate motion|
|Multi-Object Tracker||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.