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Object Detection Using Features

Detect faces and pedestrians, create customized detectors

Computer Vision Toolbox™ provides pretrained object detectors and the functionality to train a custom detector. The cascade object detector uses the Viola-Jones algorithm to detect people's faces, noses, eyes, mouth, or upper body. The people detector detects people in an input image using the histogram of oriented gradients (HOG) features and a trained support vector machine (SVM) classifier.

You can customize the cascade object detector using the trainCascadeObjectDetector function.


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ocrRecognize text using optical character recognition
readAprilTagDetect and estimate pose for ApriltTag in image
readBarcodeDetect and decode 1-D or 2-D barcode in image
acfObjectDetectorDetect objects using aggregate channel features
peopleDetectorACFDetect people using aggregate channel features
vision.CascadeObjectDetectorDetect objects using the Viola-Jones algorithm
vision.ForegroundDetectorForeground detection using Gaussian mixture models
vision.PeopleDetectorDetect upright people using HOG features
vision.BlobAnalysisProperties of connected regions
trainACFObjectDetectorTrain ACF object detector
trainCascadeObjectDetectorTrain cascade object detector model
trainImageCategoryClassifierTrain an image category classifier
detectBRISKFeaturesDetect BRISK features and return BRISKPoints object
detectFASTFeaturesDetect corners using FAST algorithm and return cornerPoints object
detectHarrisFeaturesDetect corners using Harris–Stephens algorithm and return cornerPoints object
detectKAZEFeaturesDetect KAZE features
detectMinEigenFeaturesDetect corners using minimum eigenvalue algorithm and return cornerPoints object
detectMSERFeaturesDetect MSER features and return MSERRegions object
detectORBFeaturesDetect and store ORB keypoints
detectSURFFeaturesDetect SURF features and return SURFPoints object
extractFeaturesExtract interest point descriptors
matchFeaturesFind matching features
evaluateDetectionMissRateEvaluate miss rate metric for object detection
evaluateDetectionPrecisionEvaluate precision metric for object detection
bbox2pointsConvert rectangle to corner points list
bboxOverlapRatioCompute bounding box overlap ratio
bboxPrecisionRecallCompute bounding box precision and recall against ground truth
selectStrongestBboxSelect strongest bounding boxes from overlapping clusters
selectStrongestBboxMulticlassSelect strongest multiclass bounding boxes from overlapping clusters


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Blob AnalysisStatistics for labeled regions
2-D CorrelationCompute 2-D correlation of two input matrices
Find Local MaximaFind local maxima in matrices
Gaussian PyramidPerform Gaussian pyramid decomposition


Get Started

Get Started with the Image Labeler

Interactively label rectangular ROIs for object detection, pixels for semantic segmentation, and scenes for image classification.

Point Feature Types

Choose functions that return and accept points objects for several types of features

Coordinate Systems

Specify pixel Indices, spatial coordinates, and 3-D coordinate systems

Choose Function to Visualize Detected Objects

Compare visualization functions.

Local Feature Detection and Extraction

Learn the benefits and applications of local feature detection and extraction

OCR and Language Data Support Files

Support for OCR Language data files

Detection and Classification

Train a Cascade Object Detector

Train a custom classifier

Image Retrieval with Bag of Visual Words

Retrieve images from a collection of images similar to a query image using a content-based image retrieval (CBIR) system.

Image Classification with Bag of Visual Words

Use the Computer Vision Toolbox functions for image category classification by creating a bag of visual words.

Featured Examples