detect

Detect objects using ACF object detector configured for monocular camera

Description

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

example

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

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

[___] = detect(___,Name,Value) specifies options using one or more Name,Value pair arguments. For example, detect(detector,I,'WindowStride',2) sets the stride of the sliding window used to detect objects to 2.

Examples

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Configure an ACF object detector for use with a monocular camera mounted on an ego vehicle. Use this detector to detect vehicles within video frames captured by the camera.

Load an acfObjectDetector object pretrained to detect vehicles.

detector = vehicleDetectorACF;

Model a monocular camera sensor by creating a monoCamera object. This object contains the camera intrinsics and the location of the camera on the ego vehicle.

focalLength = [309.4362 344.2161];    % [fx fy]
principalPoint = [318.9034 257.5352]; % [cx cy]
imageSize = [480 640];                % [mrows ncols]
height = 2.1798;                      % height of camera above ground, in meters
pitch = 14;                           % pitch of camera, in degrees
intrinsics = cameraIntrinsics(focalLength,principalPoint,imageSize);

monCam = monoCamera(intrinsics,height,'Pitch',pitch);

Configure the detector for use with the camera. Limit the width of detected objects to a typical range for vehicle widths: 1.5–2.5 meters. The configured detector is an acfObjectDetectorMonoCamera object.

vehicleWidth = [1.5 2.5];
detectorMonoCam = configureDetectorMonoCamera(detector,monCam,vehicleWidth);

Load a video captured from the camera, and create a video reader and player.

videoFile = fullfile(toolboxdir('driving'),'drivingdata','caltech_washington1.avi');
reader = vision.VideoFileReader(videoFile,'VideoOutputDataType','uint8');
videoPlayer = vision.VideoPlayer('Position',[29 597 643 386]);

Run the detector in a loop over the video. Annotate the video with the bounding boxes for the detections and the detection confidence scores.

cont = ~isDone(reader);
while cont
   I = reader();

   % Run the detector.
   [bboxes,scores] = detect(detectorMonoCam,I);
   if ~isempty(bboxes)
       I = insertObjectAnnotation(I, ...
                           'rectangle',bboxes, ...
                           scores, ...
                           'Color','g');
   end
   videoPlayer(I)
   % Exit the loop if the video player figure is closed.
   cont = ~isDone(reader) && isOpen(videoPlayer);
end

Input Arguments

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ACF object detector configured for a monocular camera, specified as an acfObjectDetectorMonoCamera object. To create this object, use the configureDetectorMonoCamera function with a monoCamera object and trained acfObjectDetector object as inputs.

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

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

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 Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: 'NumScaleLevels',4

Number of scale levels per octave, specified as the comma-separated pair consisting of 'NumScaleLevels' and 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 the comma-separated pair consisting of 'WindowStride' and 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 the comma-separated pair consisting of 'SelectStrongest' and either 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 the comma-separated pair consisting of 'MinSize' and a [height width] vector. 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 the comma-separated pair consisting of 'MaxSize' and a [height width] vector. 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 the comma-separated pair consisting of 'Threshold' and 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

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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. A higher score indicates higher confidence in the detection.

Introduced in R2017a