MOVING OBJECTS SEGMENTATION AND ITS APPLICATIONS
description
Transcript of MOVING OBJECTS SEGMENTATION AND ITS APPLICATIONS
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MOVING OBJECTS SEGMENTATION AND ITS APPLICATIONS
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Proposed Algorithm
1.smoothing process
2.moving algorithm
3.template matching scheme
4.background estimation
5.post-processing
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Smoothing Processing
取出 Y, C b, C r
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Smoothing Processing
Median filtering to smooth Y
Result the processed Y’
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Smoothing Processing
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Moving Object Segmentation
Adopt a spatial-temporal approach to segment object
X-y-t to x-t image
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3D-2D
Y = 179 Row data of x-t means a pixel
320*240*180 180*320
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Moving or static pixel
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Refinement algorithm
M1(x,t), M2(x,t) and M3(x,t) correspond to red, green and blue channels
moving (f(x,t)=1) or static (f(x,t)=0)
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Refinement algorithm
L pixels (L frame length) in a row data
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Minimun squared error
The problem of Eq.(5) is solved by using the pseudoinverse operation, which is based on minimum squared-error (MSE) method [8]. The solution W is formulated as,
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Pseudoinverse
M† is called the pseudoinverse of matrix M defined as,
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Moving or static pixel
原 :
改 :
Moving piexl
static piexl
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Threshold
calculate the means μ and variances σ2 2 of state values
pixel
State value
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Gaussian distribution of two states
Probability,p(x|s)
State value
Static pixel
Moving pixel
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Discriminate function g(x)
Threshold = 0.39m
Weighting value :
[ω1 , ω2 , ω3 ]
=[0.0002,-0.0326,0.0315]
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X-T marked graph
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X-Y marked graph
Original x-y marked image
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Multiple object detection
Start frame
End frame
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Search template
Color different
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Search template
u,v 搜尋範圍
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Search template-min
Then refine the marked values b(x,y) of current frame,
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Background estimation
Based on x-t sliced image
If moving pixel a(x,t)=1
If static pixel a(x,t)=0
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Post-processing
=>
By template
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Morphology modification
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Result
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Result
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Video edit
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Video edit
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