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* *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:原: 改:改: Moving piexl static piexl

Threshold calculate the means μ and variances σ 2 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 ： [ ω 1, ω 2, ω 3 ] =[0.0002, ,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

Video edit

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