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Hierarchical Part-Based Human Body Pose Estimation

Hierarchical Part-Based Human Body Pose Estimation. * Ram anan Navaratnam * Arasanathan Thayananthan † Prof. Phil Torr * Prof. Roberto Cipolla. * University Of Cambridge † Oxford Brookes University. Introduction. Input. Introduction. Input. Output. Overview. Motivation

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Hierarchical Part-Based Human Body Pose Estimation

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  1. Hierarchical Part-Based Human Body Pose Estimation * Ramanan Navaratnam * Arasanathan Thayananthan † Prof. Phil Torr * Prof. Roberto Cipolla * University Of Cambridge † Oxford Brookes University

  2. Introduction Input

  3. Introduction Input Output

  4. Overview • Motivation • Hierarchical parts • Template search • Pose estimation in a single frame • Temporal smoothing • Summary & Future work

  5. Overview • Problem motivation ??? • Hierarchical parts • Template search • Pose estimation in a single frame • Temporal smoothing • Summary & Future work

  6. Overview • Problem motivation ??? • Hierarchical parts • Template search • Pose estimation in a single frame • Temporal smoothing • Summary & Future work

  7. Overview • Problem motivation ??? • Hierarchical parts • Template search • Pose estimation in a single frame • Temporal smoothing • Summary & Future work

  8. Motivation • ‘Real-time Object Detection for Smart Vehicles’ – D. M. Gavrila & V. Philomin (ICCV 1999) • ‘Filtering using a tree-based estimator’ – Stenger et.al. (ICCV 2003)

  9. Motivation • ‘Real-time Object Detection for Smart Vehicles’ – D. M. Gavrila & V. Philomin (ICCV 1999) • ‘Filtering using a tree-based estimator’ – Stenger et.al. (ICCV 2003) • Exponential increase of templates with dimensions

  10. Motivation • ‘Pictorial Structures for Object Recognition’ – P. Felzenszwalb & D. Huttenlocher (IJCV 2005) • ‘Human upper body pose estimation in static images’ – M.W. Lee & I. Cohen (ECCV 2004)

  11. Motivation • ‘Pictorial Structures for Object Recognition’ – P. Felzenszwalb & D. Huttenlocher (IJCV 2005) • ‘Human upper body pose estimation in static images’ – M.W. Lee & I. Cohen (ECCV 2004) • Part based approach • Assembling parts together is complex

  12. Motivation • ‘Automatic Annotation of Everyday Movements’ – D. Ramanan & D. A. Forsyth (NIPS 2003) • ‘3-D model-based tracking of humans in action:a multi-view approach’ – D. M. Gavrila & L. S. Davis (CVPR 1996)

  13. Motivation • ‘Automatic Annotation of Everyday Movements’ – D. Ramanan & D. A. Forsyth (NIPS 2003) • ‘3-D model-based tracking of humans in action:a multi-view approach’ – D. M. Gavrila & L. S. Davis (CVPR 1996) • ‘State space decomposition’

  14. Hierarchical Parts

  15. Hierarchical Parts

  16. Hierarchical Parts

  17. Hierarchical Parts

  18. Hierarchical Parts Conditional prior p(xi/xparent(i)) Spatial dimensions (translation) Joint Angles

  19. Hierarchical Parts Head and torso Upper arm Lower Arm True Positive False Positive

  20. Hierarchical Parts Part Detections Head and torso 56 61 Detection Threshold = 0.81

  21. Hierarchical Parts Part Detections Head and torso 56 61 Lower arm 13 199 44 993 Detection Threshold = 0.81

  22. Template Search

  23. Template Search

  24. Template Search

  25. Template Search • Features • Chamfer distance • Appearance

  26. Template Search • Features • Chamfer distance • Appearance

  27. Template Search • Features • Chamfer distance • Appearance

  28. Template Search • Features • Chamfer distance • Appearance

  29. Template Search • Features • Chamfer distance • Appearance

  30. Template Search • Features • Chamfer distance • Appearance

  31. Template Search • Features • Chamfer distance • Appearance

  32. Template Search • Features • Chamfer distance • Appearance

  33. Template Search • Features • Chamfer distance • Appearance

  34. Template Search • Learning Appearance • Match ‘T’ pose based on edge likelihood only in initial frames • Update 3D histograms in RGB space that approximates P(RGB/part) and P(RGB)

  35. Pose Estimation in a Single Frame

  36. Pose Estimation in a Single Frame

  37. Pose Estimation in a Single Frame

  38. Temporal Smoothing HMM

  39. Temporal Smoothing T = t HMM

  40. Temporal Smoothing Viterbi back tracking HMM

  41. Temporal Smoothing Viterbi back tracking

  42. Temporal Smoothing

  43. Summary & Future work Summary • Realtime process (unoptimized code at 1Hz, 2.4 Ghz IG RAM) • 3D pose • Automatic initialisation and recovery from failure

  44. Summary & Future work Summary • Realtime process (unoptimized code at 1Hz, 2.4 Ghz IG RAM) • 3D pose • Automatic initialisation and recovery from failure Future work • Extend robustness to illumination changes • Non-fronto-parallel poses • Poses when arms are inside the body silhouette • Simple gesture recognition by assigning semantics to regions of articulation space

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