shadow resistant video tracking
DESCRIPTION
Shadow Resistant Video Tracking. Hao Jiang and Mark S. Drew School of Computing Science Simon Fraser University Vancouver, BC, Canada. Problem Statement. We want to realize this!. Traditional Contour Tracking based on motions. Outline. - PowerPoint PPT PresentationTRANSCRIPT
Shadow Resistant Video Tracking
Hao Jiang and Mark S. Drew
School of Computing Science
Simon Fraser University
Vancouver, BC, Canada
Problem Statement
Traditional ContourTracking based onmotions
We want to realize this!
Outline
We present an invariant image model. We study how to project an image to an invariant space, such that the shadow can be greatly attenuated.
We present two new external forces to the snake model and present an chordal snake model to deal with object tracking in cluttering environment.
The first external force is based on predictive contour
The chordal constraint based on a new shape descriptor
Results and conclusion
Invariant Image
T
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kT
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kik
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k
2
51
0
)()(
)()()()(
xna
For narrow bandSensors:
n
ai
Lambertian Surface
)()( kkk qQ
The responses:
Planckian Lighting
x
Considering 3-sensor cameras, = R, = G, = B
Let r=log(R/G) , b=log(B/G)
1 2 3
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12135
122
5211
5322
5233 )]
)(
)(log([)
)(
)(log(
Sq
Sqr
Sq
Sqb
r
b
The slope is determined by the camera sensors
ref-1
ref-2
…
Lighting
Material
We get,
Invariant Image Generation
r
b
Invariant Image Generation
o
Camera characteristicorientation
(r,g,b)
(log(r/g), log(b/g))
Projection
Camera Calibration
Take image of one scene under different lightings
Shift the center ofthe log-log ratios corresponding
to each material to the origin
Stack the log-ratio vectors of each material
into a matrix A and do SVD A=UDV’Camera Orientation=
V(:,1) Characteristic Orientation of Canon ES60
For Real Image
Original image Invariant image
Inertia Snake Tracking I
A predictive contour constraint
s
dssXPsCsXEsXsX ))(())(),((2
|)(|2
|)(|2
min 222
0)()( XCXPXX ssssss
If we choose quadratic norm for E(.,.) the Eular Equation,
)()( XCXFXXt
Xssssss
By introducing a artificial parameter t, the equation can be solved by PDE
Inertia Term
A Chordal ConstraintNow we further introduce a second constraint to maintain the solidness of the shape of the contour by maintaining the value of a shape descriptor.The shape discriptor is defined as
d(s, )=||X(s)-X(s+ )|| where s in is the normalized length from one point on the contour.
Apparently, d(x, ) is periodic for both s and .
s
0 1
1
The Shape descriptor
2/11
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1
0
2
1
0
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0)1,0[
]),([)(
])(
),(
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),([max),(
dsdsddStd
dsddStd
sd
dStd
sdYXSimilarity
Y
Y
X
X
ddDStd
D
DStd
DYXSimilarity s
Y
sY
X
sX ]||)(||
||),(||
||)(||
||),(||[),(
1
0
1
0
The similarity of contour X and Y is,
In frequency domain
0 1
1s
The Chordal Snake
dssdsYsXG
sYPsXPsDsYEsCsXE
sYsXsYsXs
))(||,)()((||
))(()(())(),(())(),(((2
)|)(||)((|2
)|)(||)((|2
min 222222
Here we use a simple version d(s)=||X(s)-X(s+1/2)||
The variational problem is
Where Y(s) is an accessory contour, d(s) is the calculated fromlast video frame. The corresponding reaction-diffusion PDE is:
)||(||||||
)()()(
)||(||||||
)()()(
dYXYX
YXYDYFYY
t
Y
dYXYX
XYXCXFXX
t
X
ssssss
ssssss
The Chordal Snake
Now we set Y0(s)= X0(s+1/2), D0(s)= C0(s+1/2). It is not difficultto prove the following Lemma and theorem.
Lemma: If Y(s,t1)= X(s+1/2,t1), D(s,t1)= C(s+1/2,t1) then Y(s,t)= X(s+1/2,t) for any t>= t1
Theorem: Given the initial conditions of Y0(s)= X0(s+1/2), D0(s)= C0(s+1/2), we have,
)||),2/1((||||),2/1(||
)),(),2/1((
)()(
dtsXXtsXX
tsXtsX
XCXFXXt
Xssssss
Predictive Constraint
Shape descriptor constraint
Chordal Snake Tracking II
Smoothedpredictive contour
Initial contourPrevious contour
Predictive contour
Real object boundary
Features
Chordal constraint
The System
Affine Motion Estimation
warping
Fn
Fn-1
Motion Detection
InvariantImage
InvariantImage
Motion Detection
Inertia Snake Tracking
MotionMap
ContourPrediction
Cn-1 GVF
Init Contour Pred Contour External Force
CnChordal Model
An Example
Initial Contour
Prediction Contour
Experiment Result
Two successive frames
Motion map in original color space Motion map in invariant color space
Experiment Result
Two successive frames
Motion map in original color space Motion map in invariant color space
Traditional Snake Model
Frame 1 Frame 2 Frame 3
Frame 4 Frame 5 Frame 6
Frame 7 Frame 8 Frame 9
Tracking Result
Ball Sequence Hand Sequence Baby Sequence
Conclusion
We present scheme to get shadow invariant image.
We present a much more robust snake model.
The proposed method can work well even though there is strong distracting shadows
Current framework can be easily extended to the cases when the object is passing casting shadows
Future WorkStudy scheme to deal with tracking in high dynamic range environment
Study shadow resistant method for active appear model