Video Labeler

Label video for computer vision applications


The Video Labeler app enables you to label ground truth data in a video, in an image sequence, or from a custom data source reader. Using the app, you can:

To learn more about this app, see Get Started with the Video Labeler.

Open the Video Labeler App

  • MATLAB® Toolstrip: On the Apps tab, under Image Processing and Computer Vision, click the app icon.

  • MATLAB command prompt: Enter videoLabeler.


Related Examples

Programmatic Use

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videoLabeler opens a new session of the app, enabling you to label ground truth data in a video or image sequence.

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

Example: videoLabeler('vipmen.avi')

videoLabeler(imageSeqFolder) opens the app and loads the image sequence from the input folder. An image sequence is an ordered set of images that resemble a video. The images must be the same size. 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.

To label a collection of unordered images that can vary in size, use the Image Labeler app instead.

videoLabeler(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 images and their corresponding timestamps into the app.

imageDir = fullfile(toolboxdir('vision'),'visiondata','NewTsukuba');
timeStamps = seconds(1:150);

videoLabeler(gtSource) opens the app and loads the data source and corresponding timestamps from a groundTruthDataSource object, gtSource. To generate this object for a custom data source, you can specify a custom reader function. For details, see Use Custom Data Source Reader for Ground Truth Labeling.

videoLabeler(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.


  • The built-in automation algorithms support the automation of rectangular ROI labels only. When you select a built-in algorithm and click Automate, scene labels, pixel ROI labels, polyline ROI labels, sublabels, and attributes are not imported into the automation session. To automate the labeling of these features, create a custom automation algorithm. See Create Automation Algorithm for Labeling.

  • Pixel ROI labels do not support sublabels or attributes.

  • The Label Summary window does not support sublabels or attributes


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


The Video Labeler app provides built-in algorithms that you can use to automate labeling. From the app toolstrip, click Select Algorithm, and then select an automation algorithm.

Built-In Automation AlgorithmDescription

ACF People Detector

Detect and label people using a pretrained detector based on aggregate channel features (ACF). With this algorithm, you do not need to draw any ROI labels.

Point Tracker

Track and label one or more rectangular ROI labels over short intervals using the Kanade-Lucas-Tomasi (KLT) algorithm.

Temporal Interpolator

Estimate ROIs in intermediate frames using the interpolation of rectangular ROIs in key frames. Draw ROIs on a minimum of two frames (at the beginning and at the end of the interval). The interpolation algorithm estimates the ROIs between the frames.

ACF Vehicle Detector (requires Automated Driving Toolbox™)

Detect and label vehicles using a pretrained detector based on ACF. With this algorithm, you do not need to draw any ROI labels.

Introduced in R2018b