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

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

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

ST-Patch Features appearance information motion information [7]

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

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

Detection of moving object

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

Experimental Results

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

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

Experimental Results -- ceiling light scene

Experimental Results Outdoor Scene

Experimental Results