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

Funktionen

alle erweitern

Extract Video Training Data

`groundTruth`	Ground truth label data
`writeVideoScenes`	Write video sequence to video file (Seit R2021b)
`sceneTimeRanges`	Time ranges of scene labels from ground truth data (Seit 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 (Seit R2021a)
`splitlabels`	Find indices to split labels according to specified proportions (Seit R2021a)

Design Video Classifier

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

Train Video Classifier

`predict`	Compute video classifier predictions (Seit R2021b)
`forward`	Compute video classifier outputs for training (Seit 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 (Seit R2019b)
`centerCropWindow2d`	Create rectangular center cropping window (Seit R2019b)

Classify Video

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

Visualize Classification Results

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

Themen

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

Enthaltene Beispiele

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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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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