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Spatial Histograms for Head Tracking

Spatial Histograms for Head Tracking. Sriram Rangarajan Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634. Overview of tracker. Intensity Gradients (works on the boundary of the ellipse). Modules that are complementary to gradients : Color histograms

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Spatial Histograms for Head Tracking

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  1. Spatial Histograms for Head Tracking Sriram Rangarajan Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634

  2. Overview of tracker Intensity Gradients (works on the boundary of the ellipse) Modules that are complementary to gradients : Color histograms Spatiograms Co-occurrence matrices Log-Gabor histograms Haar histograms Edge-orientation histograms Complementary module (works inside the ellipse)

  3. Gradient module Likelihood score Gradient score Normal to points on ellipse [Stan Birchfield, 1998]

  4. Overview of modules used Target histogram (from current frame) Model histogram (from first frame) Similarity measure Likelihood score from module Convert to percentage score, combine with intensity gradient module likelihood and update “state”.

  5. Similarity measure between model and target histograms Histogram intersection [Swain & Ballard1991] Likelihood normalization

  6. Overview of modules

  7. Color Histograms • Ignore spatial information (most cases) • Computationally efficient, simple, robust and invariant to any one-to-one spatial transformations

  8. Computing color histograms Pixels in a bin Number of bins for channel C1 Index for color channel Single color channel of image

  9. Spatiograms • Higher-order histograms that capture spatial information globally • Captures both values of pixels and a limited amount of their spatial relationship • Bins are weighted by mean and covariance of pixels contributing to it [Birchfield and Rangarajan, CVPR 2005]

  10. Spatiograms and histograms A histogram (no spatial information) A histogram (no spatial information) A histogram (no spatial information) Σ Σ Σ A spatiogram (some spatial Information) A spatiogram (some spatial Information) A spatiogram (some spatial Information) µ µ µ

  11. An illustrative example Three poses of a head Image generated from histogram Image generated from spatiogram

  12. Co-occurrence matrices • Used for texture analysis • Captures the local spatial relationships between colors (or gray levels) • Normally used for gray-level images No. of pixel pairs with value (x,y)

  13. Co-occurrence matrices Local spatial relationships (C) 10 11 13 10 11 10 10 13 10 10 13 10 11 11 13 10 11 13 Co-occurrence matrix Image Color values (C)

  14. Texture histograms = * Filter bank Histogram Image (Haar Wavelets or Log-Gabor filters)

  15. Haar histograms • Histogram of image after convolving with 3-level Haar pyramid: Haar histogram (at scale S and orientation O.) Image obtained by convolving with Haar pyramid at scale S and orientation O

  16. Log-Gabor histograms • Similar to Haar histograms, but uses a bank of log-Gabor filters. Log-Gabor histogram Image obtained by convolving with filter bank at scale S and orientation O

  17. Edge-orientation histograms • Obtained from gradient information • Complete reliance on spatial information • Histogram bin is decided by orientation of a pixel

  18. Computing edge-orientation histograms Difference of Gaussian kernel (DoG) = * Image Edge-orientation Histogram

  19. Edge-orientation histograms • Computed from gradient images obtained by convolving image with Difference of Gaussian (DoG) kernel in x and y • Orientation for pixel along vertical direction is 0

  20. Results: log-Gabor histograms Legend: log-Gaborhistogram colorhistogram

  21. Results: Haar histograms Legend: Haarhistogram colorhistogram

  22. Results: Edge-orientation histograms Legend: Edge-orientationhistogram colorhistogram

  23. Results: Spatiograms Legend: spatiograms colorhistogram

  24. Results: Co-occurrence matrices Legend: Co-occurrence matrices colorhistogram

  25. Overview of results

  26. Mean errors in x and y for Sequence 1

  27. Mean errors in x and y for Sequence 2

  28. Conclusion • Limited amount of spatial information drastically improves tracking results • Color information also important: • With only spatial information: tracker is distracted by cluttered background • With only color: tracker is distracted by skin-colored background • Global spatial information is the most effective (spatiograms)

  29. Thank You!

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