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