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Introduction of Computer VisionPowerPoint Presentation

Introduction of Computer Vision

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Pedestrian Detection

- Finding People in Images and Videos
- Navneet DALAL
- http://lear.inrialpes.fr/people/dalal/NavneetDalalThesis.pdf
- Chapter 4: Histogram of Oriented Gradients Based Encoding of Images
- http://lear.inrialpes.fr/pubs/2005/DT05/cvpr2005_talk.pdf
- Histograms of Oriented Gradients for Human Detection http://lear.inrialpes.fr/pubs/2005/DT05/hog_cvpr2005.pdf

Datasets

- INRIA Person Dataset
- http://pascal.inrialpes.fr/data/human/

- Computer Vision Datasets
- http://clickdamage.com/sourcecode/cv_datasets.html

Today’s Topic

- Motion Detection

Motion Detection

- Temporal Differencing
- Take the difference between two temporally adjacent frames. The difference is the moving pixels (almost). The static background results in zeros.
- Can adapt to changing lighting conditions because the difference frames are only 1/30 of a second apart (typical video 30 frames per second – 30 fps)

Motion Detection

- Temporal Differencing Issues
- Not all the relevant pixels extracted
- Background pixels extracted.

Motion Detection

Frame at time t Frame at time t+1 Frame Difference

Red Block appears

as two separate objects

Motion DetectionDifference Method

Sidewalk 12_5

Processing Video in Matlab

%C:\Program Files\MATLAB\R2008b\toolbox\OCR\BackgroundAnalysis02_02_2010

bkg = 20; %frames of video to be processed

fname = 'Office1.avi';

vidObj = mmreader(fname);

%Play video

implay('Office1.avi');

nFrames = vidObj.NumberOfFrames;

rw = vidObj.Height;

cl = vidObj.Width;

numFrames = 1000;

CODE TO PROCESS FRAMES HERE

Writing Frames to Directory

imwrite(objBox,['.\video\','LabScene','.',num2str(i),'.jpg'],'jpg');

imwrite(objBoxVideo,['.\video\','LabSceneColor','.',num2str(i),'.jpg'],'jpg');

MATLAB vidObj = mmreader(fname);

%motionDet.m

fname = 'sidewalk11_23indeo.avi';

a = aviread(fname); %%OLD METHOD

frameInfo = aviinfo(fname);

totalFrames = frameInfo.NumFrames

for i = 1:50

%for i = 1:totalFrames-1

currentFrameDiff = abs(im2double(a(1,i+1).cdata)-im2double(a(1,i).cdata));

movDiff(i) = im2frame (currentFrameDiff);

end

%MATLAB Movie file

figure, movie(movDiff)

% FOR AVI MOVIE

%movie2avi(movDiff,'sidewalk12_05_07.avi','compression', 'none');

Processing .avi files

%readWriteAviFiles

fname = 'CarsTarget2.avi';

% extracting the frame information.

%frameInfo = aviinfo( strcat( pathname, fname ));

frameInfo = aviinfo( fname );

disp( frameInfo );

for cnt = 1:20

mov1=aviread(fname,cnt);

frame1 = mov1(1,1).cdata; %uint8

image1= im2double(frame1);

figure,imshow(image1);

%WRITE INDIVIDUAL FRAMES TO DIRECTORY

imwrite(image1,['.\video\','CarVideo','.',num2str(cnt),'.jpg'],'jpg');

end

Create Video from Individual Frames

- VirtualDub

Motion Detection

- Background Modeling
- Model background without moving objects
- Represent each pixel in the frame with a 3D Gaussian – mean red, green, blue and covariance matrix
- For each pixel, collect n pixel triplets.
- Use triplets to estimate mean and covariance matrix
- Process future frames by determining the probability of each pixel in the new frame
- Threshold the probability, p(r,c)>thres is a foreground pixel (moving object)

- Compare pixel values in current frame and estimate if pixel is represented by background distribution or more likely from a different distribution (therefore new object not in background)

- Model background without moving objects

Updating Gaussian Distributions

- Small changes in the environment will result in thresholding errors
- Adapt the Gaussian models by calculating a weighted average
- Estimate means and covariance matrix from initial frames
- Update distributions using pixels identified as background – distributions will adjust for slight changes in lighting conditions

- Instead of using estimated covariance matrix use the identity matrix
- How does this change affect performance??

Pixel Modeling identity matrixStationary Camera

Sidewalk Threshold

Overpass identity matrix

Object Tracking Overpass

Face Tracking Example – All Objects identity matrix

Face Tracking Example – Faces Only identity matrix

Non-stationary Camera identity matrix

- Example: A camera panning a scene
- One approach is to register the adjacent frames
- Find key points in adjacent frames
- Determine offset
- Adjust images so that they overlap
- Take difference

NEED TO FIND CORRESPONDENCE BETWEEN FEATURE POINTS IN TWO DIFFERENT IMAGES

- Cannot match individual pixels
- Need to use a window containing many pixels (5x5, 7x7, 21x21, etc)

- Matching on a continuum like texture and edges not very robust
- Many edges (and parts of edges) will match

- At the very least:
- Need to find interest points
- Extract patches around interest points
- Match patches

Feature Points / Correspondence robust

- Points should be extracted consistently over different views
- Points should be invariant to scaling, rotation, changes in illumination
- Information in the neighborhood of the point should be unique so that it can be matched

Select Window Region robustMatch Region in Second Image

Calculate difference between the two patches

WindowMatching_ACV.m

Convert to Grayscale robust

Randomly Select Patch robust

patch = I(80:110,200:230);

For Demonstration Use Only Strip Containing Patch

Normalized Cross Correlation (Refer to equation on page p313)Also MATLAB docs

- w is template
- w is average value of elements in template
- f is the image
- f is the average of the image where f and w overlap
- Denominator normalizes resulting in an output range of -1, +1
- High value for absolute value of output is a good match

MATLAB Cross Correlation Function p313)g = normxcorr2(template, f)

Find Max Value in |g| p313)

d = abs(g);

[ypeak, xpeak] = find(d == max(d(:)));

%Adjust location by size of template

ypeak = ypeak-(size(patch,1)-1)/2;

xpeak = xpeak-(size(patch,2)-1)/2;

fprintf('\n Center of Patch: ypeak is

%d and xpeak is %d \n\n\n', ypeak, xpeak);

figure, imshow(Igray)

hold on

plot(xpeak, ypeak, 'ro')

Red – strongest response p313)

Green – second strongest response

But How Do We Select Points? p313)

- Junctions or Corners
- Stable over changes in viewpoint

Feature Points? p313)

Moravec p313)’s Corner Detector

- Overview:
- Select window size
- Shift window over image region
- If window over uniform region, shifts in all directions will result in small changes
- If window over edge, shifts along edge will results in small changes, but shifts across edge will result in large changes
- along edge – no change
- Perpendicular to edge – large change

- If window over corner, than shifts in all directions will result will result in large changes

- Detect corner by finding regions that have large changes in all directions

Subtract First Window from Second Window p313)

Window moved vertically, no change

Window moved horizontally, no change

Window moved in either

direction, large change

Harris Points p313)

Corner Response function, C:

C = det(A) – αtrace2(A), where A is the autocorrelation matrix

.

Fig. 1: Autocorrelation matrix,

where w(x, y) is the window function and I(x, y) is the image

REF: image from Wikipedia

Corner Detector p313)

Rotated Image p313)

Non-stationary Camera p313)

- Example: A camera panning a scene
- One approach is to register the adjacent frames
- Find key points in adjacent frames
- Determine offset
- Adjust images so that they overlap
- Take difference

Two Sequential Frames - Color p313)

What if you just simply take the difference between two adjacent frames?

abs(frame11-frame10) p313)

Overall approach p313)

Points of Interest p313)

Correspondence p313)

Difference p313)

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