Main Content

detect

Detect objects using ACF object detector

collapse all in page

Syntax

bboxes = detect(detector,I)
[bboxes,scores] = detect(detector,I)
detectionResults = detect(detector,ds)
[___]= detect(detector,I,roi)
[___] = detect(___,Name=Value)

Description

bboxes = detect(detector,I) detects objects within image I using the input aggregate channel features (ACF) object detector. The locations of objects detected are returned as a set of bounding boxes.

[bboxes,scores] = detect(detector,I) also returns the detection scores for each bounding box.

example

detectionResults = detect(detector,ds) detects objects within all the images returned by the read function of the input datastore.

[___]= detect(detector,I,roi) detects objects within the rectangular search region specified by roi, using either of the preceding syntaxes.

[___] = detect(___,Name=Value) specifies options using one or more name-value arguments in addition to any combination of arguments from previous syntaxes. For example, WindowStride=2 sets the stride of the sliding window used to detects objects to 2.

Examples

collapse all

Open Live Script

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')

Prefix the full path to the stop sign images.

stopSigns = fullfile(toolboxdir('vision'),'visiondata',stopSignsAndCars{:,1});

Create datastores to load the ground truth data for stop signs.

imds = imageDatastore(stopSigns);
blds = boxLabelDatastore(stopSignsAndCars(:,2));

Combine the image and box label datastores.

ds = combine(imds,blds);

Train the ACF detector. Set the number of negative samples to use at each stage to 2. You can turn off the training progress output by specifying Verbose=false,as a Name-Value argument.

acfDetector = trainACFObjectDetector(ds,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 21.5905 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)

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

Input Arguments

collapse all

ACF object detector, specified as an acfObjectDetector object. To create this object, call the trainACFObjectDetector function with training data as input.

Input image, specified as a real, nonsparse, grayscale or RGB image.

Data Types: uint8 | uint16 | int16 | double | single

Datastore, specified as a datastore object containing a collection of images. Each image must be a grayscale or RGB. The function processes only the first column of the datastore, which must contain images and must be cell arrays or tables with multiple columns. Therefore, datastore read function must return image data in the first column.

Search region of interest, specified as an [x y width height] vector. The vector specifies the upper left corner and size of a region in pixels.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: WindowStride=2 sets the stride of the sliding window used to detects objects to 2.

Number of scale levels per octave, specified a positive integer. Each octave is a power-of-two downscaling of the image. To detect people at finer scale increments, increase this number. Recommended values are in the range [4, 8].

Stride for the sliding window, specified as a positive integer. This value indicates the distance for the function to move the window in both the x and y directions. The sliding window scans the images for object detection.

Select the strongest bounding box for each detected object, specified as true or false.

  • true — Return the strongest bounding box per object. To select these boxes, detect calls the selectStrongestBbox function, which uses nonmaximal suppression to eliminate overlapping bounding boxes based on their confidence scores.

  • false — Return all detected bounding boxes. You can then create your own custom operation to eliminate overlapping bounding boxes.

Minimum region size that contains a detected object, specified as a vector of the form [height width]. Units are in pixels.

By default, MinSize is the smallest object that the trained detector can detect.

Maximum region size that contains a detected object, specified as a vector of the form [height width]. Units are in pixels.

To reduce computation time, set this value to the known maximum region size for the objects being detected in the image. By default, 'MaxSize' is set to the height and width of the input image, I.

Classification accuracy threshold, specified as a numeric scalar. Recommended values are in the range [–1, 1]. During multiscale object detection, the threshold value controls the accuracy and speed for classifying image subregions as either objects or nonobjects. To speed up the performance at the risk of missing true detections, increase this threshold.

Output Arguments

collapse all

Location of objects detected within the input image, returned as an M-by-4 matrix, where M is the number of bounding boxes. Each row of bboxes contains a four-element vector of the form [x y width height]. This vector specifies the upper left corner and size of that corresponding bounding box in pixels.

Detection confidence scores, returned as an M-by-1 vector, where M is the number of bounding boxes. Scores are returned in the range [-inf inf]. A higher score indicates higher confidence in the detection.

Detection results, returned as a 3-column table with variable names, Boxes, Scores, and Labels. The Boxes column contains M-by-4 matrices, of M bounding boxes for the objects found in the image. Each row contains a bounding box as a 4-element vector in the format [x,y,width,height]. The format specifies the upper-left corner location and size in pixels of the bounding box in the corresponding image.

Extended Capabilities

Version History

Introduced in R2017a

See Also

Apps

Functions

Objects