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