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

Region-Level Motion-Based Background Modeling and Subtraction Using MRFs. Shih-Shinh Huang Li-Chen Fu Pei-Yung Hsiao 2007 IEEE. Abstract.

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

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  1. Region-Level Motion-Based BackgroundModeling and Subtraction Using MRFs Shih-Shinh Huang Li-Chen Fu Pei-Yung Hsiao 2007 IEEE

  2. 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.

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

  4. 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.

  5. Several kinds of environment variations • Illumination Variation Gradual illumination variation Sudden illumination variation Shadow • Motion VariationGlobal motion Local motion

  6. System Overview

  7. REGION-BASED MOTION SEGMENTATION motion vector

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

  9. 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.

  10. Boundary Determination • Merge uncertain pixels to one of the markers.

  11. BACKGROUND 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.

  12. Adaptive Gaussian Mixture Models

  13. Bhattacharyya Distance

  14. 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.

  15. An example of shadow effect

  16. 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.

  17. MRFs Framework

  18. Region Classification

  19. RESULTS

  20. 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.

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