tracking using the kalman filter. point tracking estimate the location of a given point along a...
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![Page 1: Tracking using the Kalman Filter. Point Tracking Estimate the location of a given point along a sequence of images. (x 0,y 0 ) (x n,y n )](https://reader034.vdocuments.net/reader034/viewer/2022042821/56649d565503460f94a34e0c/html5/thumbnails/1.jpg)
Tracking
using the
Kalman Filter
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Point Tracking
Estimate the location of a given point along a sequence of images.
(x0,y0)
(xn,yn)
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Object Tracking
– Generate some conclusions about the motion of the scene, objects, or the camera, given a sequence of images.
– Knowing this motion, predict where things are going to project in the next image, so that we don’t have so much work looking for them.
– For example- unstable camera + Walking man:
a. Stabilize the camera using the dominant motion ( find motion parameters ! )
b. Assume that the man translates horizontally.
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Modeling “noise” or “uncertainty”
rotation
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The General Model
kkkk wxAx 1
kkkk vxHz
Dynamics Process noise~N(0,Q)
ProjectionMeasurement noise
~N(0,R)
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Prediction
kk xAx ˆˆ 1
QApAp Tkk 1
Estimated state
Estimated uncertainty / noise
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Update
Updated state
Updated uncertainty / noise
)ˆ(ˆˆ 1111 kkkk xHzKxx
11 )( kk pKHIp
111 )(
RHPHHPK Tk
Tk
The weighting factor
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kk xAx ˆˆ 1
QApAp Tkk 1
)ˆ(ˆˆ 1111 kkkk xHzKxx
11 )( kk pKHIp
111 )(
RHPHHPK Tk
Tk
Prediction Update
Summery
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Gaussian: “Normal” distribution
• 1D Gaussian:
• General Gaussian:
),(~ vNx
v
xx
eexp 2
)(
2
)( 2
2
2
)(
TxVxexp )()(2/1 1
)(
),(~ VNx
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Adding two information sources
• We are given to information sources: Z1 and Z2
• Both are normally distributed (v1 > v2)
• We would like to believe more to Z2, but still use the information from Z1 !
• Mathematically:
),(~)|( 111 vNZxp ),(~)|( 222 vNZxp
)|()|(),|( 2121 zxpzxpzzxp
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The solution
1
21
2
)(
1)|( v
x
ezxp
2
22
2
)(
2 )|( v
x
ezxp
2
22
1
21
2
)(
2
)(
2121 )()(),( v
zx
v
zx
eexzpxzpxzzp
),(~),|( 21 vNZZxp
221
11
21
2 vv
v
vv
v
21
111
vvv
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The solution (cont’)
)( 121 k
21
21
vv
vvv
21
1
vv
vK
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The merging of two Gaussians
A “noisy” measure, be don’t believe it very much
A more reliable measure
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The merging of two Gaussians (cont’)
The result is a new Gaussian with a smaller variance than the original ones !
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Why to use the normal distribution?
• Simple to manipulate
• Minimize the squared error.
• The “big numbers” low.
• The distribution of many “natural” things.
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What happens when we have a “wrong” estimation of the measurements variance ?
The correct variance (The same variance that was used to simulate the points)
The variance is too small:The estimation doesn’t converge
The variance is too large:The convergence is very slow
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Tracking using the Kalman Filter Two more examples.
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The General Model
kkkk wxAx 1
kkkk vxHz
Dynamics Process noise
ProjectionMeasurement noise
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Example 1: Estimating a constant
kk xx 1 )0,( kk wIA
kkk vxz
Measurement noise
)( IHk
1, Rzx kk
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Prediction: kk xx ˆˆ 1
kk pp 1Update
)ˆ(ˆˆ 1111 kkkk xzKxx
11 )1( kk pKp
11
1
kk
k
VP
PK
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We can combine the prediction and update
)ˆ(ˆˆ 11 kkkk xzKxx
kk pKp )1(1
1
kk
k
VP
PK
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Claim1:
Claim2:
Conclusion:The Kalman filter gives a weighted
mean !
k
i ik VP 0
11
i
k
i ik
k ZVP
X
0
1ˆ
i
k
i iik Z
VVX
0
1)
/1
1(ˆ
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Example 2 : Shihab4
In X: constant velocityIn Y: constant
acceleration
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Example2 -dynamics
kkk tvyy 1
kkk tavv 1
kk aa 1
kkk tuxx 1
kk uu 1
k
k
k
k
k
k
u
x
a
v
y
X
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Example2 -measurements
• For each possible location, give a score• Normalize the sum of the scores to 1.• The result is a matrix of “probabilities” for
each location.• Fit a 2D Gaussian to this matrix, whose
center is given by:
Given an image of the missile (or other source of information):
k
kk y
xZ