Moving Object Detection with Background Model based on spatio-Temporal Texture

Ryo Yumiba, Masanori Miyoshi,Hirononbu FujiyoshiWACV 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

• SP-patch have been used for several applications.• Calculated as statistical values of pixel grayscale

gradients within a small patch.• Let be a spatio-temporal gadients

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

• Step1: Extract ST-patch at each block of each frame• Step2: Compare with background model• Step3: moving object• Step4: number of detected block > moving object candidate flag is set ON• Step5: flag in step4 stays ON more than

active alarm against 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

Number of normalized distribution < add new one

Means of distributions are close Weight is less than

Experimental Results

• Compare with the method use only appearance features within a patch in image.

• Parameter setting– Input : 320×240, 30fps– Patch size : 15*15*5

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