Region-Level Motion-Based Background Modeling and Subtraction Using MRFs

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Region-Level Motion-Based BackgroundModeling and
Subtraction Using MRFs

• Shih-Shinh Huang
• Li-Chen Fu
• Pei-Yung Hsiao
• 2007 IEEE

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Abstract

• This paper presents a new approach to automatic
  segmentation of foreground objects from an image
  sequence by integrating techniques of background
  subtraction and motion-based foreground
  segmentation.

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Outline

• INTRODUCTION
• REGION-BASED MOTION SEGMENTATION
• BACKGROUND MODELING
• MRFS-BASED CLASSIFICATION
• RESULTS
• CONCLUSION

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INTRODUCTION

• 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.

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Several kinds of environment variations

• Illumination Variation Gradual illumination
  variation Sudden illumination variation Shadow
• Motion Variation Global motion Local motion

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System Overview
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REGION-BASED MOTION SEGMENTATION
motion vector
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Region Projection

• Projecting regions in the previous frame to the
  current one, is to facilitate the segmentation.

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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.

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Boundary Determination

• Merge uncertain pixels to one of the markers.

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BACKGROUND MODELING

• A brief description of Stauffer and Grimsons
  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.

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Adaptive Gaussian Mixture Models
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Bhattacharyya Distance
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Shadow 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.

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An example of shadow effect
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MRFS-BASED CLASSIFICATION

• Incorporate the background model to classify
  every region in the segmentation map SM into
  either a foreground object or a background one by
  MRFs.

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MRFs Framework
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Region Classification
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RESULTS
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CONCLUSION

• 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.