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Background Subtraction

Background Subtraction. Various Methods for Different Inputs. Purpose of Background Subtraction. Reduce problem set for further processing Only process part of picture that contains the relevant information Segment the image into foreground and background Add a virtual background.

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Background Subtraction

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  1. Background Subtraction Various Methods for Different Inputs Nathan Johnson

  2. Purpose of Background Subtraction • Reduce problem set for further processing • Only process part of picture that contains the relevant information • Segment the image into foreground and background • Add a virtual background Nathan Johnson

  3. Encountered Problems • Lighting • Shadows • Gradual/Sudden illumination changes • Camouflage • Moving objects • Foreground aperture • Foreground object becomes motionless • Bootstrapping Nathan Johnson

  4. Lighting and Shadows • Weight the luminance with other characteristics • Depth of object • Region/Frame information • Adjust the background model with time • Store a history of previous backgrounds Nathan Johnson

  5. Comparison of Two Techniques • Wallflower • Uses three different components • Pixel, Region, and Frame levels • Uses many different statistical models to anticipate various changes in the background • Gordon, Darrell, Harville, Woodfill Subtraction • Two or more cameras to measure distances • Uses distance to determine foreground and falls back on luminance Nathan Johnson

  6. Wallflower Method – Pixel Level • Makes initial judgment whether a pixel is in the foreground • Handles background model adaptation • Addresses many of the classical problems • Moved objects • Time of day • Camouflage • Bootstrapping Nathan Johnson

  7. Wallflower – Region & Frame • Region level • Refines the pixel level judgment • Handles foreground aperture problem • Frame level • Sudden frame level change • Uses previous models to figure out what caused the sudden change • Light switching on/off Nathan Johnson

  8. Results using Wallflower Images from Wallflower: Principles and Practice of Background Maintenance, Kentaro Toyama, John Krumm, Barry Brumitt, Brian Meyers

  9. Gordon, et al. Method • Correctly identifies background depth and color when it is represented in a minority of the frames • Addition of range solves many of the classic problems • Shadows • Bootstrapping • Foreground object becomes motionless Nathan Johnson

  10. Obtaining Initial Background Model • Records the (R,G,B,Z) values at each pixel • Attempts to determine background through the observed depth • Marks a pixel as invalid if there is not enough information for the range • valid pixel – range determines whether the pixel is in the background, without the aid of the (R,G,B) values • invalid pixel – fall back on classic methods for background subtraction Nathan Johnson

  11. Gordon, et al. Method (cont.) • rm is invalid • ri is valid and smoothly connected to regions with valid background data then a foreground decision can be made • Solves the problem of the background being the same depth as part of the foreground • Z-keying* methods fail in these cases *Kanade, Yoshida, Oda, Kano, and Tanaka, “A Video-Rate Stereo Machine and Its New Applications”, Computer Vision and Pattern Recognition Conference, San Francisco, CA, 1996. Nathan Johnson

  12. Gordon, et al. Method (cont.) • YValid(Ym) = Y > Ymin • Shadows have a stronger effect on luminance than inter-reflections • Separate ratio limits for shadows and reflections Nathan Johnson

  13. Problems Using Only Range or Color Images from Background estimation and removal based on range and color, G. Gordon, T.Darrell, M. Harville, J. Woodfill

  14. Which is better? • Wallflower over Gordon, et al. • Doesn’t require extra cameras to record depth • Gordon, et al. produces a “halo” around foreground objects • Gordon, et al. over Wallflower • Handles more problems • Tree waving • Bootstrapping Nathan Johnson

  15. Other Innovative Methods • Fast, Lighting Independent Background Subtraction • Advantages • Light has no basis on the decision of foreground • Disadvantages • Requires a known, static background • Multiple cameras Nathan Johnson

  16. Which Method to Use • Type of background present • Static or Dynamic • Lighting • Gradual/Sudden changes • Lack of lighting • Hardware used during recording • Multiple cameras • Speed required for application Nathan Johnson

  17. Conclusion • Record as much information as possible • Background subtraction methods have mainly been looked at in particular situations • Severe case: Fast, Lighting Independent Method • A method to use in every case is still being researched • Currently combinations of previously released methods offer the best results for background subtraction Nathan Johnson

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