Adaptive Motion Segmentation for Changing Background

Yepeng Guan

doi:10.4236/jsea.2009.22014

Abstract

Segmentation of moving objects efficiently from video sequence is very important for many applications. Background subtraction is a common method typically used to segment moving objects in image sequences taken from a statistic camera. Some existing algorithms cannot adapt to changing circumstances and require manual calibration in terms of specification of parameters or some hypotheses for changing background. An adaptive motion segmentation method is developed according to motion variation and chromatic characteristics, which prevents undesired corruption of the background model and does not consider the adaptation coefficient. RGB color space is selected instead of introducing complex color models to segment moving objects and suppress shadows. A color ratio for 4-connected neighbors of a pixel and multi-scale wavelet transformation are combined to suppress shadows. The mentioned approach is scene-independent and high correct segmentation. It has been shown that the approach is robust and efficient to detect moving objects by experiments.

Highlights

Moving objects segmentation is an important topic in computer vision applications, including video conferences, vehicle tracking, and three-dimensional object identification, and has been actively investigated in recent years [1]
Background subtraction is a common method typically used to segment moving objects in image sequences taken from a statistic camera
An adaptive motion segmentation method is developed according to motion variation and chromatic characteristics, which prevents undesired corruption of the background model and does not consider the adaptation coefficient

Summary

Introduction

Moving objects segmentation is an important topic in computer vision applications, including video conferences, vehicle tracking, and three-dimensional object identification, and has been actively investigated in recent years [1]. A critical situation occurs whenever moving objects stop for a long time and become a part of the background. An approach to adaptive background updating and shadow suppressing is developed. RGB color space is selected instead of introducing complex color models to segment moving objects. The main contribution of the proposal is that the developed approach is sceneindependent and automatic background updating according to motion variations caused by moving objects. The second contribution is that when segmenting motion objects it does not require any complex supervised training or manual calibration in terms of the specification of parameters or makes any hypotheses. The developed approach is efficient and flexible during segmenting moving objects and suppressing shadows in applications.

Related Works

Background

Shadow Suppression

Experimental Results

TP GT TP GT

Conclusions