Color Invariant Motion Detection

8/9/2019 Color Invariant Motion Detection

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Color-Invariant Motion Detectionunder Fast Illumination Changes

Paper by:Ming Xu and Tim EllisCIS 750

Presented by: Xiangdong Wen

Advisor: Prof. Latecki


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Agenda

>Introduction

>Color Fundamentals>Color-Invariant Motion Detection>Experimental Results

>Discussion>Conclusion


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Introduction>Motion detection algorithms: based on the differencing operation of

image intensities between a frame and a background image.>Background image: reflects the static elements in a scene.>Background image needs to be updated because

>Lack of a target-free training period;>Gradual illumination variations;>Background objects which then move.

>Updating schemeLinear interpolation between the previous bg value and new observation

>Gaussian mixture model: based on gray-level or RGB color intensities

>could detect a large proportion of changes.>cannot follow fast illumination changes:

>moving clouds,>long shadows,>switching of artificial lighting


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>Marchant and Onyango.(2000) proposed a physics-based method for shadow compensation in scenes illuminated by daylight.>Represented the daylight as a black body,

>Assumed the color filters to be of infinitely narrow bandwidth.Results: as illumination changes, the ratio (R/B)/(G/B)^A depends on

surface reflection only. ( A can be calculated from daylight model andcamera.)

>Finlayson et al.(2000) Using same scheme.

Results: Log-Chromaticity Differences (LCDs) ln(R/G) and ln(B/g) areindependent of light intensity and there exists a weighted combinationof LCDs which is independent of light intensity and light color

Identifying a particular object surface under

varying illumination


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Motion detection in outdoor environments

illuminated by daylight

>A refection model influenced by ambient objects is used.>large-scale illumination changed mainly arises from

varying cloud cover.>The dominant illumination comes from either directsunlight or reflection from clouds

>The normalised rgb color space is used to eliminate the

influence of varying illumination>A Gaussian mixture model is used to model each pixel of

the background , provides multi-background modellingcapabilities for complex out scenes


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Colour FundamentalsAn image taken with a color camera is composed of sensor

responses as:

->illumination,

-> wavelength,

->reflectance of an object surface

->Camera sensitivity

The image intensity:

>The appearance of objects is a result of interaction betweenillumination and reflectance.>To track the object surface, it is desirable to separate thevariation of the illumination from that of the surface reflection.


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Shadow model(1)

In an out door environment, fast illumination changes occur at the regions where shadows emerge or disappear.>large-scale (arising from moving cloud)>small-scale (from objects themselves)

Shadow model (Gershon et al 1986)>There is only one illuminant in the scene>Some of the light does not reach the object because blocking objects.>create a shadow region and a directly lit region on the

object.>The shadow region is illuminated by each reflectionobjects j:


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Shadow model cont.>The reflected light from the object surface

>For the directly lit region

>For the shadow region:

>Assume the chromatic average of the ambient objects is gray

i.e. it is relatively balanced in all visible wavelengths and

Where c is independent of and may varies over space


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Shadow model cont.The assumption is realistic for the fast-moving cloud case, in which the

only illuminant is the sunlight and both the blocking and ambientobjects are gray(or white) clouds.

Under the assumption, the reflected light from directly lit and shadowregions will stay in proportion for a given object surface. Thus theimage intensities at all color channels being in proportion no matter litor shadowed

The proportionality between RGB color channels can be represented usingthe normalised color components :

Where each component of will keep constant for a given objectsurface under varying illumination.


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Color-Invariant Motion Detection

A single Gausssion is sufficient to model a pixel value for one channel of the RGB components resulting from a particular surface under

particular lighting and account for acquisition noise.A single adaptive Gaussian is sufficient to model each RGB channel if

lighting changes gradually over time. The estimated background valueis interpolated between the previous estimation and the newobservation. It cannot follow an RGB component under fastillumination changes. A normalized color component (rgb) for a givenobject surface tends to be constant under lighting changes and isappropriate to model using an adaptive Gaussion.

Multiple adaptive Gaussians (a mixture of Gaussions) are used to model a pixel at which multiple object surfaces may appear as the backgrounds.E.g. swaying trees


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Color-Invariant Motion Detection cont.

Let the pixel value at time t be and modeled by amixture of N Gaussian distributions. The probability of observing the

pixel value is:

Where G is the Gaussian probability density function of the I-th backgroundBi, P(Xi | Bi)

P(Bi) reflecting the likelihood that the distribution accounts for the observeddata.


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SchemeEvery new observation, Xt, is checked against the N Gaussian

distributions, A match is defined as an observation within about 3standard deviations of a distribution. If none of the N distributionsmatch the current pixel value, the least probable distribution isreplaced by the new observation.

For the matched distribution, i, the parameters are updated as:

For the unmatched distribution

The distribution(s) with greatest weight is(are)considered as the background model.


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Experimental Results

To assess the significance of the color-invariant motion detection>Evaluated the model at both pixel and frame levels using a set of image sequences.

>The image sequence was captured at a frame rate of 2 hz>Each frame was compressed in JPEG format>frame size: 384x288 pixels.

This sequence well represents the abundant contexts of a day lit outdoor environment:>Fast illumination changes, waving trees, shading of tree canopies,>Highlights of specula reflection, as well as pedestrians.


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the absolute (RGB) and normalised (rgb) color components at selected pixelsthrough time.(a) No foreground object is present.(b) Foreground object are present.The absolute color components (RGB) change greatly with the illumination.

The normalized color components (rgb) for a background pixel have flat profiles under illumination.

For each foreground pixel, at least one rgb component appears as an apparentspike.


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The parameter updating procedure of the Gaussian background modelfor one color component

(a) A lit region with foreground objects

(b) A shadowed region without foreground objects

The thin lines represent and (upper and lower) profiles,respectively.


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Comparing the RGB and rgb results under little illumination change

>The results are coherent.

>Because of the different emphasis of image contexts, the blobsappear as different shapes.


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The RGB and rgb results under a major illumination change.(a) A large area of the background is detected as a huge

foreground object.

(c) Ground truth targets are clearly visible under fastillumination changes


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Discusion(1)>Appropriate selection of the initial deviation

>An underestimate of the initial deviation prohibits many ground truth background pixels from beingadapted into background models

>An overestimate of the initial deviation

needs a longer learning period at the start of an image sequence>Currently

it is manually selected and globally uniform according to thenoise level in shaded regions where the absolute noise level inrgb components is high.

>In futureit may be automatically selected according to the localspatial variation in the rgb components at the start time


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Discussion(2)

>The rgbl color space>combined the intensity I, with the rgb color space>is an invertible transformation from RGB space>avoids the loss of the intensity information.>robustly determinates the shadowed region:

>the rgb components are stable>the I component is significantly lower.

>Two kinds of pixels which may be excluded from consideration:>RGB components saturated can make the corresponding rgbcomponents unconstrained

>The rgb components in over-dark regions are very noisy.>To alleviate this problem:> Using cameras with auto iris control> Gamma correction


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Conclutions

>A Gaussion mixture model based on the rgb color space has been presented for maintaining a

background image for motion detection.>The scheme is especially successful when applied

to outdoor scenes illuminated by daylight and isrobust to fast illumination changes arising frommoving cloud and self-shadows.

>The success results from a realistic reflection modelin which shadows are present.


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Thank you!

Documents

Color Invariant Motion Detection