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Moving Object Detection with Background Model based on spatio- Temporal Texture Ryo Yumiba, Masanori Miyoshi,Hirononbu Fujiyoshi WACV 2011.

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Presentation on theme: "Moving Object Detection with Background Model based on spatio- Temporal Texture Ryo Yumiba, Masanori Miyoshi,Hirononbu Fujiyoshi WACV 2011."— Presentation transcript:

1 Moving Object Detection with Background Model based on spatio- Temporal Texture Ryo Yumiba, Masanori Miyoshi,Hirononbu Fujiyoshi WACV 2011

2 Outline Introduction ST-patch features Background subtraction – Generation of background model – Moving object detection – Update background model Experimental result

3 Introduction Background subtraction is a common method for detecting moving object. – Advantage: not requiring previous knowledge of moving object – Problem: cannot discriminate moving objects from background when these background change significantly Two approaches to generating BG model covering changes: – Pixel-wise background model – Patch-wise background model Proposed method cover global changes by using appearance information and it cover local changes by using motion information

4 ST-Patch Features appearance information motion information [7]

5 ST-Patch Features Patch size: 15*15*5(frame) – Appearance components differ between tree and road without regard to motion – Motion components increase according to temporal change – Motion components differ from transitions of sunlight and waving of tree

6 Generation of Background Model Use Gaussian mixture distribution of ST-patch to generate background model. Parameters are calculated previously from examples of background video using EM algorithm Background changes generally differ according to location  calculate Parameters at each block

7 Detection of moving object

8 Update of Background Model It is difficult to generate a background model that wholly covers changes in background in advance  update background model during moving object detection Means of distributions are close

9 Experimental Results

10 Experimental Result -- Outdoor Scene Waving tree, sunlight Use 1179 frames without pedestrians to generate background model Regard 1359 frames as frames with moving objects

11 Experimental Results -- Outdoor Scene 289 frames # FN ↓ ( ∴ background updating)

12 Experimental Results -- ceiling light scene

13 Experimental Results Outdoor Scene

14 Experimental Results

15


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