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Ground Truth Labeler

Label ground truth data for automated driving applications


The Ground Truth Labeler app enables you to label ground truth data in multiple videos, image sequences, or lidar point clouds.

Using the app, you can:

  • Simultaneously label multiple time-overlapped signals representing the same scene.

  • Define an axis-aligned or rotated rectangular region of interest (ROI) labels, polyline ROI labels, pixel ROI labels, cuboid ROI labels for lidar labeling, and scene label definitions. Use these labels to interactively label your ground truth data.

  • Use the Projected View option to view the labels in top, front and side views simultaneously.

  • Use the XY View, YZ View, and XZ View options to get the top view, front view, and side view of a point cloud scene, respectively.

  • Use built-in detection or tracking algorithms to label ground truth data.

  • Write, import, and use custom automation algorithms to automatically label ground truth data.

  • Evaluate the performance of your label automation algorithms by using a visual summary.

  • Export the ground truth labels as a groundTruthMultisignal object. You can use this object for system verification or for training an object detector or semantic segmentation network.

  • Display time-synchronized signals, such as CAN bus data, by using the driving.connector.Connector API.

To learn more about this app, see Get Started with Ground Truth Labelling.

Ground Truth Labeler app

Open the Ground Truth Labeler App

  • MATLAB® Toolstrip: On the Apps tab, under Automotive, click the app icon.

  • MATLAB command prompt: Enter groundTruthLabeler.

Programmatic Use

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groundTruthLabeler opens a new session of the app, enabling you to label ground truth data.

groundTruthLabeler(videoFileName) opens the app and loads the input video. The video file must have an extension supported by VideoReader.

Example: groundTruthLabeler('caltech_cordova1.avi')

groundTruthLabeler(imageSeqFolder) opens the app and loads the image sequence from the input folder. An image sequence is an ordered set of images that resembles a video.

imageSeqFolder must be a string scalar or character vector that specifies the folder containing the image files. The image files must have extensions supported by imformats and are loaded in the order returned by the dir function.

groundTruthLabeler(imageSeqFolder,timestamps) opens the app and loads a sequence of images with their corresponding timestamps. timestamps must be a duration vector of the same length as the number of images in the sequence.

For example, load a sequence of road images and their corresponding timestamps into the app.

imageDir = fullfile(toolboxdir('driving'),'drivingdata','roadSequence');

groundTruthLabeler(___,'ConnectorTargetHandle',connector) opens the app and loads both of these components:

  • A video or image sequence signal, depending on the input argument combination you specify

  • An external analysis or visualization tool that is time-synchronized with the specified signal

The connector input is a handle to a driving.connector.Connector class that implements the external tool.

For example, this syntax opens the app with a video signal and synchronized lidar visualization tool.


When you have an external tool connected to a signal in the app, consider these tips.

  • If you remove the signal that is connected to the tool, the app disconnects the tool and closes it.

  • The signal connected to the tool must be the main signal, that is, the signal whose timestamps are used in the playback controls at the bottom of the app. If you change the main signal, the app disconnects the tool and closes it.

  • If you start a new app session, the app disconnects the tool and closes it.

groundTruthLabeler(sessionFile) opens the app and loads a saved app session, sessionFile. The sessionFile input contains the path and file name. The MAT-file that sessionFile points to contains the saved session.

groundTruthLabeler(gTruth) opens the app and loads a groundTruth object. The ground truth object data source must be an image sequence, video, or a custom data source.


  • Lidar signals do not support line or pixel ROI labels.

  • Pixel ROI labels do not support sublabels or attributes.

  • Cuboid ROI labels do not support sublabels.

  • The Label Summary window does not support sublabels or attributes

  • Importing groundTruth object created from Image Labeler app is not supported.

More About

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  • To avoid having to relabel ground truth with new labels, organize the labeling scheme you want to use before marking your ground truth.

  • You can copy and paste labels between signals that are of the same type.


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You can use label automation algorithms to speed up labeling within the app. To create your own label automation algorithm to use within the app, see Create Automation Algorithm for Labeling. You can also use one of the built-in algorithms by following these steps:

  1. Import the data you want to label, and create at least one label definition.

  2. On the app toolstrip, click Select Algorithm and select one of the built-in automation algorithms.

  3. If you imported multiple signals, click Select Signals and, in the Select Signals window, select one or more signals to automate. Click OK.

  4. Click Automate, and then follow the automation instructions in the right pane of the automation window.

Version History

Introduced in R2017a

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