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Train ACF-Based Stop Sign Detector

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Use training data to train an ACF-based object detector for stop signs

Add the folder containing images to the MATLAB path.

imageDir = fullfile(matlabroot, 'toolbox', 'vision', 'visiondata', 'stopSignImages');
addpath(imageDir);

Load ground truth data, which contains data for stops signs and cars.

load('stopSignsAndCarsGroundTruth.mat','stopSignsAndCarsGroundTruth')

View the label definitions to see the label types in the ground truth.

stopSignsAndCarsGroundTruth.LabelDefinitions
ans=3×3 table
        Name          Type        Group  
    ____________    _________    ________

    {'stopSign'}    Rectangle    {'None'}
    {'carRear' }    Rectangle    {'None'}
    {'carFront'}    Rectangle    {'None'}

Select the stop sign data for training.

stopSignGroundTruth = selectLabelsByName(stopSignsAndCarsGroundTruth,'stopSign');

Create the training data for a stop sign object detector.

trainingData = objectDetectorTrainingData(stopSignGroundTruth);
summary(trainingData)
trainingData: 41x2 table

Variables:

    imageFilename: cell array of character vectors
    stopSign: cell

Statistics for applicable variables:

                     NumMissing

    imageFilename        0     
    stopSign             0

Train an ACF-based object detector.

acfDetector = trainACFObjectDetector(trainingData,'NegativeSamplesFactor',2);
ACF Object Detector Training
The training will take 4 stages. The model size is 34x31.
Sample positive examples(~100% Completed)
Compute approximation coefficients...Completed.
Compute aggregated channel features...Completed.
--------------------------------------------
Stage 1:
Sample negative examples(~100% Completed)
Compute aggregated channel features...Completed.
Train classifier with 42 positive examples and 84 negative examples...Completed.
The trained classifier has 19 weak learners.
--------------------------------------------
Stage 2:
Sample negative examples(~100% Completed)
Found 84 new negative examples for training.
Compute aggregated channel features...Completed.
Train classifier with 42 positive examples and 84 negative examples...Completed.
The trained classifier has 20 weak learners.
--------------------------------------------
Stage 3:
Sample negative examples(~100% Completed)
Found 84 new negative examples for training.
Compute aggregated channel features...Completed.
Train classifier with 42 positive examples and 84 negative examples...Completed.
The trained classifier has 54 weak learners.
--------------------------------------------
Stage 4:
Sample negative examples(~100% Completed)
Found 84 new negative examples for training.
Compute aggregated channel features...Completed.
Train classifier with 42 positive examples and 84 negative examples...Completed.
The trained classifier has 61 weak learners.
--------------------------------------------
ACF object detector training is completed. Elapsed time is 18.9743 seconds.

Test the ACF-based detector on a sample image.

I = imread('stopSignTest.jpg');
bboxes = detect(acfDetector,I);

Display the detected object.

annotation = acfDetector.ModelName;
I = insertObjectAnnotation(I,'rectangle',bboxes,annotation);

figure 
imshow(I)

Figure contains an axes object. The hidden axes object contains an object of type image.

Remove the image folder from the path.

rmpath(imageDir);