Video Classification

Perform video classification and activity recognition using deep learning

Classify the activity or action contained in a sequence of images from visual data sources, such as a video stream, into a set of categories using deep learning. Vision-based activity recognition involves predicting the action within a sequence of images, such as walking, swimming, or sitting, using a set of video frames. Activity recognition from video has many applications, such as human-computer interaction, anomaly detection, and surveillance. To learn more, see Getting Started with Video Classification Using Deep Learning.

Apps

Video Labeler	Label video for computer vision applications
Ground Truth Labeler	Label ground truth data for automated driving applications

Functions

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Extract Video Training Data

`groundTruth`	Ground truth label data
`writeVideoScenes`	Write video sequence to video file (Since R2021b)
`sceneTimeRanges`	Time ranges of scene labels from ground truth data (Since R2021b)

Load Video Training Data

`VideoReader`	Create object to read video files
`fileDatastore`	Datastore with custom file reader
`transform`	Transform datastore
`combine`	Combine data from multiple datastores
`folders2labels`	Get list of labels from folder names (Since R2021a)
`splitlabels`	Find indices to split labels according to specified proportions (Since R2021a)

Design Video Classifier

`inflated3dVideoClassifier`	Inflated-3D (I3D) video classifier. Requires Computer Vision Toolbox Model for Inflated-3D Video Classification (Since R2021b)
`slowFastVideoClassifier`	SlowFast video classifier. Requires Computer Vision Toolbox Model for SlowFast Video Classification (Since R2021b)
`r2plus1dVideoClassifier`	R(2+1)D video classifier. Requires Computer Vision Toolbox Model for R(2+1)D Video Classification (Since R2021b)

Train Video Classifier

`predict`	Compute video classifier predictions (Since R2021b)
`forward`	Compute video classifier outputs for training (Since R2021b)

Augment and Preprocess Training Data

`imwarp`	Apply geometric transformation to image
`imcrop`	Crop image
`imresize`	Resize image
`randomAffine2d`	Create randomized 2-D affine transformation
`centerCropWindow2d`	Create rectangular center cropping window

Classify Video

`classifyVideoFile`	Classify a video file (Since R2021b)
`classifySequence`	Classify video sequence (Since R2021b)
`updateSequence`	Update video sequence for classification (Since R2021b)
`resetSequence`	Reset video sequence properties for streaming video classification (Since R2021b)

Visualize Classification Results

`vision.VideoPlayer`	Play video or display image
`vision.DeployableVideoPlayer`	Display video
`insertText`	Insert text in image or video

Topics

Getting Started with Video Classification Using Deep Learning
Video recognition and classification, analyze, classify, and track actions contained in visual data sources.

Featured Examples

Activity Recognition from Video and Optical Flow Data Using Deep Learning

Train an inflated-3D (I3D) two-stream convolutional neural network for activity recognition using RGB and optical flow data from videos.

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Activity Recognition Using R(2+1)D Video Classification

Train an R(2+1)D video classifier for activity recognition.

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Gesture Recognition using Videos and Deep Learning

Train a SlowFast convolutional neural network for gesture recognition using RGB data from videos.

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