acfObjectDetector
Detect objects using aggregate channel features
Description
The acfObjectDetector object detects objects from an image, using
the aggregate channel features (ACF) object detector. To detect objects in an image,
pass the trained detector to the detect
function.
The ACF object detector recognizes specific objects in images, based on the training
images and the object ground truth locations used with the trainACFObjectDetector function.
Creation
Create an acfObjectDetector object by calling the trainACFObjectDetector function with training
data.
detector = trainACFObjectDetector(trainingData,...)
Properties
Object Functions
detect | Detect objects using ACF object detector |
Examples
Train a Stop Sign Detector Using an ACF Object Detector
Use the trainACFObjectDetector with training images to create an ACF object detector that can detect stop signs. Test the detector with a separate image.
Load the training data.
load('stopSignsAndCars.mat')Select the ground truth for stop signs. These ground truth is the set of known locations of stop signs in the images.
stopSigns = stopSignsAndCars(:,1:2);
Add the full path to the image files.
stopSigns.imageFilename = fullfile(toolboxdir('vision'),... 'visiondata',stopSigns.imageFilename);
Train the ACF detector. You can turn off the training progress output by specifying 'Verbose',false as a Name,Value pair.
acfDetector = trainACFObjectDetector(stopSigns,'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 19.2848 seconds.
Test the ACF detector on a test image.
img = imread('stopSignTest.jpg');
[bboxes,scores] = detect(acfDetector,img);Display the detection results and insert the bounding boxes for objects into the image.
for i = 1:length(scores) annotation = sprintf('Confidence = %.1f',scores(i)); img = insertObjectAnnotation(img,'rectangle',bboxes(i,:),annotation); end figure imshow(img)
References
[1] Dollar, P., R. Appel, S. Belongie, and P. Perona. "Fast Feature Pyramids for Object Detection." Pattern Analysis and Machine Intelligence, IEEE Transactions. Vol. 36, Issue 8, 2014, pp. 1532–1545.