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Object Detectors

Detect faces and pedestrians, create customized detectors

Computer Vision System Toolbox™ provides pretrained object detections 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 . You can also use the Image Labeler, feature extractors, and the Statistics and Machine Learning Toolbox™ classifiers to create a custom detector.


Image LabelerLabel ground truth in a collection of images


acfObjectDetectorDetect objects using aggregate channel features
peopleDetectorACFDetect people using aggregate channel features
fasterRCNNObjectDetectorDetect objects using Faster R-CNN deep learning detector
fastRCNNObjectDetectorDetect objects using Fast R-CNN deep learning detector
rcnnObjectDetectorDetect objects using R-CNN deep learning detector
vision.CascadeObjectDetectorDetect objects using the Viola-Jones algorithm
vision.ForegroundDetectorForeground detection using Gaussian mixture models
vision.PeopleDetector Detect upright people using HOG features
vision.BlobAnalysisProperties of connected regions
trainACFObjectDetectorTrain ACF object detector
trainCascadeObjectDetectorTrain cascade object detector model
trainFastRCNNObjectDetectorTrain a Fast R-CNN deep learning object detector
trainFasterRCNNObjectDetectorTrain a Faster R-CNN deep learning object detector
trainImageCategoryClassifierTrain an image category classifier
trainRCNNObjectDetectorTrain an R-CNN deep learning object detector
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
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
selectStrongestBboxSelect strongest bounding boxes from overlapping clusters


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



Train a Cascade Object Detector

Train a custom classifier

Define Ground Truth for Image Collections

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

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 System Toolbox™ functions for image category classification by creating a bag of visual words.

Digit Classification Using HOG Features

This example shows how to classify digits using HOG features and a multiclass SVM classifier.

Image Category Classification Using Bag of Features

This example shows how to use a bag of features approach for image category classification.

Image Retrieval Using Customized Bag of Features

This example shows how to create a Content Based Image Retrieval (CBIR) system using a customized bag-of-features workflow.


Local Feature Detection and Extraction

Learn the benefits and applications of local feature detection and extraction

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

Object Detection in a Cluttered Scene Using Point Feature Matching

This example shows how to detect a particular object in a cluttered scene, given a reference image of the object.

Pattern Matching

This example shows how to use the 2-D normalized cross-correlation for pattern matching and target tracking.