1. 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. 2Introduction Input

3. 3Introduction Output

4. 4Overview 1.Motivation 2.Hierarchical parts 3.Template search 4.Pose estimation in a single frame 5.Temporal smoothing 6.Summary & Future work

5. 5Overview 1.Problem motivation ??? 2.Hierarchical parts 3.Template search 4.Pose estimation in a single frame 5.Temporal smoothing 6.Summary & Future work

6. 6Overview 1.Problem motivation ??? 2.Hierarchical parts 3.Template search 4.Pose estimation in a single frame 5.Temporal smoothing 6.Summary & Future work

7. 7Overview 1.Problem motivation ??? 2.Hierarchical parts 3.Template search 4.Pose estimation in a single frame 5.Temporal smoothing 6.Summary & Future work

8. 8Motivation 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. 9Motivation Exponential increase of templates with dimensions 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)

10. 10Motivation 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. 11Motivation Part based approach Assembling parts together is complex 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)

12. 12Motivation 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. 13Motivation 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. 14 Hierarchical Parts

15. 15 Hierarchical Parts

16. 16 Hierarchical Parts

17. 17 Hierarchical Parts

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

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

20. 20 Hierarchical Parts Detection Threshold = 0.81 Detections Head and torso 6156 Part

21. 21 Hierarchical Parts Detection Threshold = 0.81 Detections Head and torso 6156 13 19944 993 Part Lower arm

22. 22 Template Search

23. 23 Template Search

24. 24 Template Search

25. 25 Template Search Features Chamfer distance Appearance

26. 26 Template Search Features Chamfer distance Appearance

27. 27 Template Search Features Chamfer distance Appearance

28. 28 Template Search Features Chamfer distance Appearance

29. 29 Template Search Features Chamfer distance Appearance

30. 30 Template Search Features Chamfer distance Appearance

31. 31 Template Search Features Chamfer distance Appearance

32. 32 Template Search Features Chamfer distance Appearance

33. 33 Template Search Features Chamfer distance Appearance

34. 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. 35 Pose Estimation in a Single Frame

36. 36 Pose Estimation in a Single Frame

37. 37 Pose Estimation in a Single Frame

38. 38 Temporal Smoothing HMM

39. 39 Temporal Smoothing HMM T = t

40. 40 Temporal Smoothing HMM Viterbi back tracking

41. 41 Temporal Smoothing Viterbi back tracking

42. 42 Temporal Smoothing

43. 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. 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