Color-Invariant Motion Detection under Fast Illumination Changes

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Color-Invariant Motion Detection under Fast
Illumination Changes

• Paper byMing Xu and Tim Ellis
• CIS 750
• Presented by Xiangdong Wen
• Advisor Prof. Latecki

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Agenda

• gtIntroduction
• gtColor Fundamentals
• gtColor-Invariant Motion Detection
• gtExperimental Results
• gtDiscussion
• gtConclusion

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Introduction

• gtMotion detection algorithms based on the
  differencing operation of image intensities
  between a frame and a background image.
• gtBackground image reflects the static elements
  in a scene.
• gtBackground image needs to be updated because
• gtLack of a target-free training period
• gtGradual illumination variations
• gtBackground objects which then move.
• gtUpdating scheme
• Linear interpolation between the previous bg
  value and new observation
• gtGaussian mixture model based on gray-level or
  RGB color intensities
• gtcould detect a large proportion of changes.
• gtcannot follow fast illumination changes
• gtmoving clouds,
• gtlong shadows,
• gtswitching of artificial lighting

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Identifying a particular object surface under
varying illumination

• gtMarchant and Onyango.(2000) proposed a
  physics-based method for shadow compensation in
  scenes illuminated by daylight.
• gtRepresented the daylight as a black body,
  
• gtAssumed 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 and
  camera.)
• gtFinlayson et al.(2000) Using same scheme.
• Results Log-Chromaticity Differences (LCDs)
  ln(R/G) and ln(B/g) are independent of light
  intensity and there exists a weighted combination
  of LCDs which is independent of light intensity
  and light color

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Adaptive schemes in color-invariant detection of
motion under varying illumination

• gtWren et al (1997) used the normalised
  components, U/Y and V/Y of YUV color space to
  remove shadows in a indoor scene
• gtA single adaptive Gaussian represents the
  probability density of pixel belonging to the
  background.
• gtThe scene without person has to be learned
  before to locate people.
• gtRaja et al (1998) used hue(H) and saturation(S)
  of an HSI color space to decouple the influence
  of illumination changes in a indoor scene,
• gtA Gaussian mixture model was used to estimate
  the probabilities of each pixel belonging to a
  multi-colored foreground object
• gtEach Gaussian models one color in the
  foreground object and was learned in a training
  stage.

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Motion detection in outdoor environments
illuminated by daylight

• gtA refection model influenced by ambient objects
  is used.
• gtlarge-scale illumination changed mainly
  arises from varying cloud cover.
• gtThe dominant illumination comes from either
  direct sunlight or reflection from clouds
• gtThe normalised rgb color space is used to
  eliminate the influence of varying illumination
• gtA Gaussian mixture model is used to model each
  pixel of the background , provides
  multi-background modelling capabilities for
  complex out scenes

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Colour Fundamentals

• An image taken with a color camera is composed of
  sensor responses as

-gtillumination,
-gtreflectance of an object surface
-gtCamera sensitivity
-gt wavelength,
The image intensity
gtThe appearance of objects is a result of
interaction between illumination and
reflectance. gtTo track the object surface, it is
desirable to separate the variation 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.
• gtlarge-scale (arising from moving cloud)
• gtsmall-scale (from objects themselves)
• Shadow model (Gershon et al 1986)
• gtThere is only one illuminant in the scene
• gtSome of the light does not reach the object
  because blocking objects.
• gtcreate a shadow region and a directly lit
  region on the object.
• gtThe shadow region is illuminated by each
  reflection objects j

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Shadow model cont.

• gtThe reflected light from the object surface
• gtFor the directly lit region

gtFor the shadow region
gtAssume 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 ambient
  objects are gray(or white) clouds.
• Under the assumption, the reflected light from
  directly lit and shadow regions will stay in
  proportion for a given object surface. Thus the
  image intensities at all color channels being in
  proportion no matter lit or shadowed
• The proportionality between RGB color channels
  can be represented using the normalised color
  components
• Where each component of will keep constant
  for a given object surface 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 value is
  interpolated between the previous estimation and
  the new observation. It cannot follow an RGB
  component under fast illumination changes. A
  normalized color component (rgb) for a given
  object surface tends to be constant under
  lighting changes and is appropriate 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 a mixture of N
  Gaussian distributions. The probability of
  observing the pixel value is
• Where G is the Gaussian probability density
  function of the I-th background Bi, P(Xi Bi)
• P(Bi) reflecting the likelihood that the
  distribution accounts for the observed data.

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Scheme

• Every new observation, Xt, is checked against the
  N Gaussian distributions, A match is defined as
  an observation within about 3 standard deviations
  of a distribution. If none of the N distributions
  match the current pixel value, the least probable
  distribution is replaced 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
• gtEvaluated the model at both pixel and frame
  levels using a set of image sequences.
• gtThe image sequence was captured at a frame
  rate of 2 hz
• gtEach frame was compressed in JPEG format
• gtframe size 384x288 pixels.
• This sequence well represents the abundant
  contexts of a day lit outdoor environment
• gtFast illumination changes, waving trees,
  shading of tree canopies,
• gtHighlights of specula reflection, as well
  as pedestrians.

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• the absolute (RGB) and normalised (rgb) color
  components at selected pixels through time.
• No foreground object is present.
• 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 apparent spike.

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• The parameter updating procedure of the Gaussian
  background model for one color component
• A lit region with foreground objects
• 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 gtThe results are
coherent. gtBecause of the different emphasis of
image contexts, the blobs appear as
different shapes.
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• The RGB and rgb results under a major
  illumination change.
• A large area of the background is detected as a
  huge foreground object.
• (c) Ground truth targets are clearly visible
  under fast illumination changes

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Discusion(1)

• gtAppropriate selection of the initial deviation
• gtAn underestimate of the initial deviation
• prohibits many ground truth
  background pixels from being
• adapted into background models
• gtAn overestimate of the initial deviation
• needs a longer learning period at the
  start of an image sequence
• gtCurrently
• it is manually selected and globally
  uniform according to the
• noise level in shaded regions where
  the absolute noise level in
• rgb components is high.
• gtIn future
• it may be automatically selected
  according to the local
• spatial variation in the rgb
  components at the start time

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Discussion(2)

• gtThe rgbl color space
• gtcombined the intensity I, with the rgb
  color space
• gtis an invertible transformation from RGB
  space
• gtavoids the loss of the intensity
  information.
• gtrobustly determinates the shadowed region
  
• gtthe rgb components are stable
• gtthe I component is significantly
  lower.
• gtTwo kinds of pixels which may be excluded from
  consideration
• gtRGB components saturated can make the
  corresponding rgb components unconstrained
• gtThe rgb components in over-dark regions are
  very noisy.
• gtTo alleviate this problem
• gt Using cameras with auto iris control
• gt Gamma correction

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Conclutions

• gtA Gaussion mixture model based on the rgb color
  space has been presented for maintaining a
  background image for motion detection.
• gtThe scheme is especially successful when applied
  to outdoor scenes illuminated by daylight and is
  robust to fast illumination changes arising from
  moving cloud and self-shadows.
• gtThe success results from a realistic reflection
  model in which shadows are present.

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Thank you!