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Shape and Dynamics in Human Movement Analysis

Shape and Dynamics in Human Movement Analysis. Ashok Veeraraghavan. Outline. Motivation What do we want to do? Shape Shape based methods for recognition Dynamics based methods for recognition Results. Motivation. Human Perception Shape or Dynamics (or is it Both??). Laurel and Hardy.

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Shape and Dynamics in Human Movement Analysis

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  1. Shape and Dynamics in Human Movement Analysis Ashok Veeraraghavan

  2. Outline • Motivation • What do we want to do? • Shape • Shape based methods for recognition • Dynamics based methods for recognition • Results

  3. Motivation • Human Perception • Shape or Dynamics (or is it Both??)

  4. Laurel and Hardy

  5. Laurel ??? Hardy ???

  6. Who is this ? ? ?

  7. Who is this ? ? ?

  8. Introduction • Psychophysics work indicates that dynamics is important for recognition in humans. • Johansson: Light Display Moving dots • Murray(1964) : 24 gait components • Cutting :Familiarity;Static Vs Dynamic • Kozlowski: dynamics speed, bounciness, rhythm. • Cutting : Dynamic Invariant • Gender Discrimination

  9. Prior Work • Image Correlation. • Silhoutte Based Nearest Neighbour. • Dynamic Time Warping • Hidden Markov Model • Model parts of human body and extract gait signature.(eg., Thigh)

  10. Most gait recognition algorithms are shape based ! • Relative importance of shape and dynamics

  11. Definition of Shape • “Shape is all the geometric information that remains when location, scale and rotational effects are filtered out from the object”. • Kendall’s Statistical Shape Theory used for the characterization of shape. • Pre-shape accounts for location and scale invariance alone.

  12. Pre-Shape • k landmark points (complex vector) • Translational Invariance: Subtract mean • Scale Invariance : Normalize the scale

  13. Feature Extraction • Silhoutte Landmarks Centered Landmarks Pre-shape vector

  14. Distance between shapes • Shape lies on a spherical manifold. • Shape distance must incorporate the non-Euclidean nature of the shape space. • 1)Full Procrustes distance. 2)Partial Procrustes distance. 3)Procrustes distance.

  15. Full Procrustes Distance • Procrustes Fit • Full Procrustes Distance=Minimum Procrustes Fit.

  16. Other shape distances • Partial procrustes distance • Procrustes distance (ρ): distance on the Great circle.

  17. Tangent Space • Linearization of spherical shape space around a particular pole. • The Procrustes mean shape is usually chosen as the pole. • If the shapes in the data are very close to each other then Euclidean distance in tangent space approximates shape distances.

  18. Shape based methods for Recognition • Stance Correlation. • Dynamic time warping in shape space. • Hidden Markov Model in shape space.

  19. Stance Correlation • Exemplars for 6 stances for each individual. • The correlation between exemplars is used as the matching criterion. • Performance comparable to Baseline.

  20. Dynamic time warping in shape space . • Enforce end-point constraint. • Obtain best warping path. • Cumulative error is computed using the shape distances described. • Performance is better than baseline.

  21. Hidden Markov Model in shape-space • Exemplars are regarded as states. • HMM built for each person in the gallery. • Identity established by maximizing the probability that the observation came from the model in the gallery. • Performance is better than baseline and comparable to DTW.

  22. Dynamical Models • Stance based AR model. • Linear Dynamical System

  23. Stance based AR model • Video sequence is clustered into 3 distinct stances. Each frame is identified as belonging to one of these three stances. • Parameters of an AR model learnt for each stance. • Model parameters used for recognition. • Performance is below baseline.

  24. Linear Dynamical System(ARMA) • Parameters (A,C) of a dynamical system learnt for each individual. • Distance between models used as score for recognition.

  25. Learning the model

  26. Distance between models • Subspace angles (θi: i=1,2….n). • Martin,Gap and Frobenius Distance.

  27. Results on USF database • Gallery 71 people. • Probe varies from Gallery in view, shoe and surface. • CMS curves shown.

  28. Sample Sequences

  29. Stance Correlation.

  30. Dynamic time warping

  31. Comparison of DTW with Baseline

  32. Stance based AR model

  33. Linear Dynamical system

  34. Comparison of various methods on the USF database.

  35. Results on the CMU database • Gallery consists of 25 people. • 3 different activities studied: Slow walk, Fast walk and walk with ball. • Recognition performed within and across activities.

  36. Percentage of Recognition using Stance correlation.

  37. Similarity Matrix using Linear Dynamical system(ARMA)

  38. Percentage of Recognition using Linear Dynamical system

  39. Mocap Data

  40. Mocap (Activity Recognition)

  41. Mocap (Activity using ARMA)

  42. Conclusions • Shape is more important for recognition than dynamics. Shape also provides for speed change invariance. • Dynamics can help to improve performance of shape based methods. • Activity Recognition: Dynamics plays a important role. • Dynamical models like ARMA can perform recognition across activities.

  43. Thank You.

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