Colorado School of Mines. Computer Vision. Professor William Hoff Dept of Electrical Engineering &Computer Science.

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1 Professor William Hoff Dept of Electrical Engineering &Computer Science 1

2 People Detection Some material for these slides comes from

3 Histogram of Oriented Gradients (HoG) The image is divided into small cells A histogram of gradient orientations is computed for each cell (weighted by gradient magnitude) 3

4 Matlab Function 4

5 Example Look at size of feature vector Effect of changing parameters clear all close all I = imread('cameraman.tif'); [featurevector, hogvisualization] = extracthogfeatures(... I,... 'CellSize', [8 8],... % Size of cell in pixels, [8 8] (default) 'BlockSize', [2 2],... % #cells in block, [2 2] (default) 'NumBins', 9,... % #orientation bins, 9 (default) 'UseSignedOrientation', false); % default is false figure, imshow(i), hold on; plot(hogvisualization); 5

6 HoG Based People Detection First used for application of person detection [Dalal and Triggs, CVPR 2005] Cited since in thousands of computer vision papers 6

7 Positive and negative examples + thousands more + millions more

8 Sliding Window Detector Detect objects by testing each subwindow Reduces object detection to binary classification 8

9 HoG templates for person detection

10 Person detection with HoG & linear SVM [Dalal and Triggs, CVPR 2005]

11 Matlab Function 11

12 Run on GRAZ 01 Database 460 images, each 640x

13 Example Program (1 of 2) clear all close all % Create PeopleDetector system object. peopledetector = vision.peopledetector(... 'ClassificationModel', 'UprightPeople_128x64',... % choices are UprightPeople_96x48, UprightPeople_128x64 'ClassificationThreshold', 0.5,... % default is 1 'MinSize', [],... % Smallest region containing a person; default is [], which is size used in training 'MaxSize', [],... % Largest region containing a person; default is [], which is size of image 'ScaleFactor', 1.05,... % scales between MinSize and MaxSize; default is 1.05 'WindowStride', [8 8],... % how far window moves, default is [8 8] 'MergeDetections', false... % merge similar detections; default is true ); % Get names of all the images in the specified folder. dirname = 'Graz_persons_samples'; % Folder name filenames = dir(sprintf('%s/*.bmp', dirname)); N = length(filenames); 13

14 % Read and process each image in turn. for i=1:n fname = sprintf('%s/%s', dirname, filenames(i).name); I = imread(fname); (2 of 2) % Run the people detector; return the bounding boxes and the scores. [bboxes, scores] = step(peopledetector, I); % Draw these objects on the image. if ~isempty(bboxes) % Create graphical bounding boxes. shapeinserter = vision.shapeinserter('bordercolor', 'Custom',... 'CustomBorderColor', [ ]); end I = step(shapeinserter, I, int32(bboxes)); I = inserttext(i,... int32(bboxes(:,1:2)),... % position scores,... % values to display 'TextColor', [ ],... 'FontSize', 16); % Display the image. imshow(i, []), title(filenames(i).name, 'Interpreter', 'none'); end %pause input('hit enter to continue', 's'); 14

15 Analysis See effect of changing the parameters Under what conditions does it seem to fail? 15

Low Resolution Detection falls off when resolution (#pixels on person) decreases Detection good for people > about 80 pixels tall Very poor for < 50 pixels tall

16 Low Resolution Detection falls off when resolution (#pixels on person) decreases Detection good for people > about 80 pixels tall Very poor for < 50 pixels tall Different resolution levels: Pedestrian with 140, 50, 20, and 10 pixels height. From: H. Sager, Pedestrian Detection In Low resolution Videos, PhD thesis,,

17 Approach Use a short sequence of images to make up for lack of information Humans can easily recognize people in image sequences Video Video From: H. Sager, Pedestrian Detection In Low resolution Videos, PhD thesis,,

18 Approach Stack HOG features from a short sequence of images (~0.5 sec) This is enough to capture the characteristic walking motion of a human From: H. Sager, Pedestrian Detection In Low resolution Videos, PhD thesis,,

20 pixels in height From Pedestrian Detection in

19 Results Method is particularly useful for videos taken from aerial vehicles These people are around 20 pixels in height From Pedestrian Detection in Low Resolution Videos Ph.D. thesis, Hisham Sager

20 Articulated Objects A single, rigid template is usually not enough to represent a category Many objects (e.g. humans) are articulated, or have parts that can vary in configuration Many object categories look very different from different viewpoints, or from instance to instance 20

Part Based Model A collection of parts arranged in a deformable configuration Star model (1 root + multiple parts) Parts filter at twice resolution of the root filter

21 Part Based Model A collection of parts arranged in a deformable configuration Star model (1 root + multiple parts) Parts filter at twice resolution of the root filter Felzenszwalb, Pedro F., et al. "Object detection with discriminatively trained part-based models." IEEE transactions on pattern analysis and machine intelligence 32.9 (2010):

22 Score of the detection: Part Based Model 22

23 Latent SVMs Rather than training a single linear SVM separating positive examples cluster positive examples into components and train a classifier for each (using all negative examples)

24 From: Felzenszwalb, Pedro F., et al. "Object detection with discriminatively trained part-based models." IEEE transactions on pattern analysis and machine intelligence 32.9 (2010):

25 Two component bicycle model side component frontal component

26 Six component car model side view frontal view root filters (coarse) part filters (fine) deformation models

27 Six component person model

Results on PASCAL 2007 dataset Examples of high scoring detections on the PASCAL 2007 dataset, selected from the top 20 highest scoring detections in each class.

28 Results on PASCAL 2007 dataset Examples of high scoring detections on the PASCAL 2007 dataset, selected from the top 20 highest scoring detections in each class. The framed images (last two in each row) illustrate false positives for each category. (From Felzenszwalb, Pedro F., et al. "Object detection with discriminatively trained part based models." IEEE transactions on pattern analysis and machine intelligence 32.9 (2010): ) 28

29 Results on PASCAL 2007 dataset Examples of high scoring detections on the PASCAL 2007 dataset, selected from the top 20 highest scoring detections in each class. The framed images (last two in each row) illustrate false positives for each category. (From Felzenszwalb, Pedro F., et al. "Object detection with discriminatively trained part based models." IEEE transactions on pattern analysis and machine intelligence 32.9 (2010): ) 29

Deformable Part Models

CS 1674: Intro to Computer Vision Deformable Part Models Prof. Adriana Kovashka University of Pittsburgh November 9, 2016 Today: Object category detection Window-based approaches: Last time: Viola-Jones