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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