region-level motion-based background modeling and subtraction using mrfs
DESCRIPTION
Region-Level Motion-Based Background Modeling and Subtraction Using MRFs. Shih-Shinh Huang Li-Chen Fu Pei-Yung Hsiao 2007 IEEE. Abstract. - PowerPoint PPT PresentationTRANSCRIPT
Region-Level Motion-Based Region-Level Motion-Based BackgroundBackground
Modeling and Subtraction Modeling and Subtraction Using MRFsUsing MRFsShih-Shinh HuangShih-Shinh Huang
Li-Chen FuLi-Chen FuPei-Yung HsiaoPei-Yung Hsiao
2007 IEEE2007 IEEE
AbstractAbstract
This paper presents a new approach to aThis paper presents a new approach to automatic segmentation of foreground outomatic segmentation of foreground objects from an image sequence by integrbjects from an image sequence by integrating techniques of background subtracating techniques of background subtraction and motion-based foreground segmtion and motion-based foreground segmentation.entation.
OutlineOutline
INTRODUCTIONINTRODUCTION REGION-BASED MOTION REGION-BASED MOTION
SEGMENTATIONSEGMENTATION BACKGROUND MODELINGBACKGROUND MODELING MRFS-BASED CLASSIFICATIONMRFS-BASED CLASSIFICATION RESULTSRESULTS CONCLUSIONCONCLUSION
INTRODUCTIONINTRODUCTION
In many applications, success of detecting foreground regions from a static background scene is an important step before high-level processing.
In real-world situations, there exist several kinds of environment variations that will make the foreground detection more difficult.
Several kinds of environment variations
Illumination VariationGradual illumination variationSudden illumination variationShadow
Motion VariationGlobal motionLocal motion
System Overview
REGION-BASED MOTION REGION-BASED MOTION SEGMENTATIONSEGMENTATION
motion vector
Region Projection
Projecting regions in the previous frame to the current one, is to facilitate the segmentation.
Motion Marker Extraction
The output of this step is a set of motion-coherent regions, all pixels within a region comply with a motion model.
Boundary Determination
Merge uncertain pixels to one of the markers.
BACKGROUND MODELINGBACKGROUND MODELING
A brief description of Stauffer and Grimson’s work is first given and then we introduce the Bhattacharyya distance as the difference measure between the region from the region-based motion segmentation and the one represented by the background model.
Adaptive Gaussian Mixture Models
Bhattacharyya Distance
Shadow effectShadow effect
However, the region similarity defined in this way will lead to misclassification of the background region where direct light is blocked by the foreground object.
An example of shadow An example of shadow effecteffect
MRFS-BASED MRFS-BASED CLASSIFICATIONCLASSIFICATION
Incorporate the background model to classify every region in the segmentation map SM into either a foreground object or a background one by MRFs.
MRFs Framework
Region Classification
RESULTSRESULTS
CONCLUSIONCONCLUSION
Experimental results demonstrate that our proposed method can successfully extract the foreground objects even under situations with illumination variation, shadow, and local motion.
Our on-going research is to develop a tracking algorithm which can be used track the detected object.