trainCascadeObjectDetector
Train cascade object detector model
Syntax
Description
trainCascadeObjectDetector(
writes a trained cascade detector XML file named, outputXMLFilename,positiveInstances,negativeImages)outputXMLFilename.
The file name must include an XML extension. For a more detailed explanation
on how this function works, refer to Train a Cascade Object Detector.
trainCascadeObjectDetector( resumes
an interrupted training session. The outputXMLFilename,'resume')outputXMLFilename input
must match the output file name from the interrupted session. All
arguments saved from the earlier session are reused automatically.
trainCascadeObjectDetector(___,
uses additional options specified by one or more Name,Value)Name,Value
pair arguments.
Examples
Train Stop Sign Detector
Load the positive samples data from a MAT file. The file contains a table specifying bounding boxes for several object categories. The table was exported from the Training Image Labeler app.
Load positive samples.
load('stopSignsAndCars.mat');Select the bounding boxes for stop signs from the table.
positiveInstances = stopSignsAndCars(:,1:2);
Add the image folder to the MATLAB path.
imDir = fullfile(matlabroot,'toolbox','vision','visiondata',... 'stopSignImages'); addpath(imDir);
Specify the folder for negative images.
negativeFolder = fullfile(matlabroot,'toolbox','vision','visiondata',... 'nonStopSigns');
Create an imageDatastore object containing negative images.
negativeImages = imageDatastore(negativeFolder);
Train a cascade object detector called 'stopSignDetector.xml' using HOG features. NOTE: The command can take several minutes to run.
trainCascadeObjectDetector('stopSignDetector.xml',positiveInstances, ... negativeFolder,'FalseAlarmRate',0.1,'NumCascadeStages',5);
Automatically setting ObjectTrainingSize to [35, 32] Using at most 42 of 42 positive samples per stage Using at most 84 negative samples per stage --cascadeParams-- Training stage 1 of 5 [........................................................................] Used 42 positive and 84 negative samples Time to train stage 1: 0 seconds Training stage 2 of 5 [........................................................................] Used 42 positive and 84 negative samples Time to train stage 2: 0 seconds Training stage 3 of 5 [........................................................................] Used 42 positive and 84 negative samples Time to train stage 3: 2 seconds Training stage 4 of 5 [........................................................................] Used 42 positive and 84 negative samples Time to train stage 4: 5 seconds Training stage 5 of 5 [........................................................................] Used 42 positive and 17 negative samples Time to train stage 5: 9 seconds Training complete
Use the newly trained classifier to detect a stop sign in an image.
detector = vision.CascadeObjectDetector('stopSignDetector.xml');Read the test image.
img = imread('stopSignTest.jpg');Detect a stop sign.
bbox = step(detector,img);
Insert bounding box rectangles and return the marked image.
detectedImg = insertObjectAnnotation(img,'rectangle',bbox,'stop sign');
Display the detected stop sign.
figure; imshow(detectedImg);
Remove the image directory from the path.
rmpath(imDir);
Input Arguments
Tips
Training a good detector requires thousands of training samples. Processing time for a large amount of data varies, but it is likely to take hours or even days. During training, the function displays the time it took to train each stage in the MATLAB® command window.
The OpenCV HOG parameters used in this function are:
Numbins:
9CellSize =
[8 8]BlockSize =
[4 4]BlockOverlap =
[2 2]UseSignedOrientation =
false
References
[1] Viola, P., and M. J. Jones. "Rapid Object Detection using a Boosted Cascade of Simple Features." Proceedings of the 2001 IEEE Computer Society Conference. Volume 1, 15 April 2001, pp. I-511–I-518.
[2] Ojala, T., M. Pietikainen, and T. Maenpaa. “Multiresolution Gray-scale and Rotation Invariant Texture Classification With Local Binary Patterns.” IEEE Transactions on Pattern Analysis and Machine Intelligence. Volume 24, No. 7 July 2002, pp. 971–987.
[3] Dalal, N., and B. Triggs. “Histograms of Oriented Gradients for Human Detection.” IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Volume 1, 2005, pp. 886–893.