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Activity and Motion Detection in Videos

Longin Jan Latecki and Roland Miezianko, Temple University

Dragoljub Pokrajac, Delaware State University

Dover, August 2005

Definition of Motion Detection

• Action of sensing physical movement in a give area

• Motion can be detected by measuring change in speed or vector of an object

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Motion Detection

Goals of motion detection • Identify moving objects• Detection of unusual activity patterns• Computing trajectories of moving objects

Applications of motion detection • Indoor/outdoor security• Real time crime detection• Traffic monitoringMany intelligent video analysis systems are based

on motion detection.

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Two Approaches to Motion Detection

• Optical Flow– Compute motion within region or the frame as a

whole

• Change detection– Detect objects within a scene– Track object across a number of frames

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Background Subtraction

• Uses a reference background image for comparison purposes.

• Current image (containing target object) is compared to reference image pixel by pixel.

• Places where there are differences are detected and classified as moving objects.

Motivation: simple difference of two images shows moving objects

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a. Original scene b. Same scene later

Subtraction of scene a from scene b Subtracted image with threshold of 100

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Static Scene Object Detection and Tracking

• Model the background and subtract to obtain object mask

• Filter to remove noise

• Group adjacent pixels to obtain objects

• Track objects between frames to develop trajectories

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Background Modelling by Michael Knowles

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Background Model

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After Background Filtering…

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Approaches to Background Modeling

• Background Subtraction

• Statistical Methods (e.g., Gaussian Mixture Model, Stauffer and Grimson 2000)

Background Subtraction:1. Construct a background image B as average of few images2. For each actual frame I, classify individual pixels as

foreground if |B-I| > T (threshold)3. Clean noisy pixels

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Background Subtraction

Background Image Current Image

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Statistical Methods

• Pixel statistics: average and standard deviation of color and gray level values (e.g., W4 by Haritaoglu, Harwood, and Davis 2000)

• Gaussian Mixture Model (e.g., Stauffer and Grimson 2000)

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Gaussian Mixture Model • Model the color values of a particular pixel as a

mixture of Gaussians• Multiple adaptive Gaussians are necessary to cope

with acquisition noise, lighting changes, etc.• Pixel values that do not fit the background

distributions (Mahalanobis distance) are considered foreground

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Gaussian Mixture Model

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Temple1 - RGB Distribution of Pixel 172x165

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Temple1 - RG Distribution of Pixel 172x165

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Block 44x42 Pixel 172x165

R-G Distribution R-G-B Distribution

VIDEO

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Proposed ApproachMeasuring Texture Change

• Classical approaches to motion detection are based on background subtraction, i.e., a model of background image is computed, e.g., Stauffer and Grimson (2000)

• Our approach does not model any background image.

• We estimate the speed of texture change.

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In our system we divide video plane in disjoint blocks(4x4 pixels), and compute motion measure for each block.

mm(x,y,t) for a given block location (x,y) is a function of t

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8x8 Blocks

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Block size relative to image size

Block 24x28

1728 blocks per frame

Image Size:36x48 blocks

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Motion Measure Computation

• We use spatial-temporal blocks to represent videos

• Each block consists of NBLOCK x NBLOCK pixels from 3 consecutive frames

• Those pixel values are reduced to K principal components using PCA (Kahrunen-Loeve trans.)

• In our applications, NBLOCK=4, K=10

• Thus, we project 48 gray level values to a texture vector with 10 PCA components

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3D Block Projection with PCA (Kahrunen-Loeve trans.)

48-component block vector (4*4*3)

-0.5221 -0.0624 -0.1734 -0.2221 -0.2621 -0.4739 -0.4201 -0.4224 -0.0734 -0.1386

10 principal components

t+1 tt-1

4*4*3 spatial-temporal blockLocation I=24, J=28,time t-1, t, t+1

Motion Measure Computation

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Texture of spatiotemporal blocks works better than color pixel values

• More robust

• Faster

We illustrate this with texture trajectories.

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PCA 1PCA 2

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Trajectory of block (24,8) (Campus 1 video)

Space of spatiotemporal block vectors

Moving blocks corresponds to regions of high local variance,i.e., higher spread

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Campus 1 video

block I=24, J=28

Standardized PCA components of RGB pixel values at pixel location (185,217) that is inside of block (24,28).

Comparison to the trajectory of a pixel inside block (24,8)

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Detection of Moving Objects Based on Local Variation

For each block location (x,y) in the video plane• Consider texture vectors in a symmetric

window [t-W, t+W] at time t• Compute the covariance matrix• Motion measure is defined as

the largest eigenvalue of the covariance matrix

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3.83.944.14.24.34.44.5

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3.1 4.2000 3.5000 2.6000 4.1000 3.7000 2.8000 3.9000 3.9000 2.9000 4.0000 4.0000 3.0000 4.1000 3.9000 2.8000 4.2000 3.8000 2.7000 4.3000 3.7000 2.6500

Feature vectors

0.0089 -0.0120 -0.0096 -0.0120 0.0299 0.0201 -0.0096 0.0201 0.0157

Covariance matrix

Feature Vectors in Space

0.0499 0.0035 0.0011 Eigenvalues

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Motion Measure

Current time

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3.83.944.14.24.34.44.5

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3.1 4.3000 3.7000 2.6500 4.4191 3.5944 2.4329 4.1798 3.8415 2.6441 4.2980 3.6195 2.5489 4.2843 3.7529 2.7114 4.1396 3.7219 2.7008 4.3257 3.6078 2.8192

Feature vectors

0.0087 -0.0063 -0.0051 -0.0063 0.0081 0.0031 -0.0051 0.0031 0.0154

Covariance matrix

Feature Vectors in Space

0.0209 0.0093 0.0020 Eigenvalues

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Motion Measure

Current time

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Graph of motion measure mm(24,8,:) for Campus 1 video

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Graph of motion measuremm(40,66) of Sub_IR_2 video

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SubIR2 Detected Motion - Block 40x66

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SubIR2 Motion Measure - Block 40x66

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Motion Measure Detected Motion

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Dynamic Distribution Learning and Outlier Detection

1)1(

)1()(C

tstd

tmeantf

2)1(

)1()(C

tstd

tmeantf

)()1()1()( tfutmeanutmean

)()( 2 ttstd

222 ))1()(()1()1()( tmeantfutut

(1)

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Detect Outlier

Switch to a nominal state

Update the estimates of mean and standard deviation only when the outliers are not detected