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Recovering Human Body Configurations: Combining Segmentation and Recognition

Recovering Human Body Configurations: Combining Segmentation and Recognition. Greg Mori, Xiaofeng Ren, Alyosha Efros and Jitendra Malik. Problem. Input image. Stick figure. Support masks. Single image: No background subtraction. Human Figures in Still Images.

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Recovering Human Body Configurations: Combining Segmentation and Recognition

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  1. Recovering Human Body Configurations: Combining Segmentation and Recognition Greg Mori, Xiaofeng Ren, Alyosha Efros and Jitendra Malik

  2. Problem Input image Stick figure Support masks Single image: No background subtraction

  3. Human Figures in Still Images • Detection of humans is possible for stereotypical poses • Standing • Walking (Mohan, Papageorgiou, and Poggio PAMI 01; Viola, Jones and Snow ICCV 03) • But we want to do more • Wider variety of poses • Localize joint positions

  4. Why Is This Hard? • Variety of poses • Clothing • Missing parts • Small support for parts • Background clutter

  5. Previous Work • Exemplar approaches • Shape Contexts (Mori and Malik ECCV 2002) • Order Structure (Sullivan and Carlson ECCV 2002) • LSH (Shakhnarovich et al. ICCV 2003) • Part-based approaches • Rectangles (Ioffe and Forsyth, IJCV 2001) • Corners (Song et al., PAMI 2003) • Dynamic programming (Felzenszwalb and Huttenlocher, CVPR 2000)

  6. Approach:Unifying Segmentation and Recognition • Bottom-up • Detect half-limbs and torsos • Top-down • Assemble parts into human figure

  7. SEGMENTS • Segmentation • How many? SUPERPIXELS Why Segmentation for Recognition? • Window-scanning (e.g. face detection)

  8. Limb/Torso Detectors • Learn limb and torso detectors from hand-labeled data • Cues: • Contour • Average edge strength on boundary • Shape • Similarity to rectangle • Shading • x,y gradients, blurred • Focus • Ratio of high to low frequency energies

  9. Islands of Saliency • “Partial configurations” • 3 half-limbs plus a torso • Combinatorial search over sets of limbs and torsos configurations

  10. Pruning Partial Configurations • Many partial configurations are physically impossible • Prune using global constraints (not a tree) • Proximity • Relative widths • Maximum lengths • Symmetry in clothing colour • Results in 1000 partial configurations

  11. Completing Configurations • Use superpixels to complete half-limbs • 2 or 3-limbed people • Sort partial configurations • Use limb, torso, and segmentation scores • Extend missing limb(s) of best configurations

  12. LU ARM RL ARM LL LEG RL LEG LL LEG RL LEG

  13. Results I

  14. Results II Rank 3 Rank 3

  15. Addressing the Challenges • Variety of poses • Parts-based approach, no restrictive priors • Clothing • Shape, shading, edge cues • Missing parts • Start with “islands of saliency” • Small support for parts • Start with “islands of saliency” • Background clutter • Global constraints on relative sizes of parts, colour

  16. All segmentations are wrong, but some segmentations are useful!

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