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Lecture 15-16 Pose Estimation – Gaussian Process

EE4-62 MLCV. Lecture 15-16 Pose Estimation – Gaussian Process. Tae-Kyun Kim. EE4-62 MLCV. MS KINECT http://www.youtube.com/watch?v=p2qlHoxPioM. The KINECT body pose estimation is achieved by randomised regression forest techniques. EE4-62 MLCV.

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Lecture 15-16 Pose Estimation – Gaussian Process

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  1. EE4-62 MLCV Lecture 15-16 Pose Estimation – Gaussian Process Tae-Kyun Kim

  2. EE4-62 MLCV MS KINECThttp://www.youtube.com/watch?v=p2qlHoxPioM The KINECT body pose estimation is achieved by randomised regression forest techniques.

  3. EE4-62 MLCV We learn pose estimation as a regression problem, and Gaussian process as a cutting edge regression method. We see it through the case study (slide credits to): Semi-supervised Multi-valued Regression, Navaratnam, Fitzgibbon, Cipolla, ICCV07, wherepractical challenges addressed are 1)multi-valued regression and 2)sparsity of data.

  4. EE4-62 MLCV A mapping function is learnt from the input image I to the pose vector , which is taken as a continuous variable.

  5. Given an image (top left) , e.g. a silhouette (top right) is obtained by background subtraction techniques (http://en.wikipedia.org/wiki/Background_subtraction). The estimated 3d pose is shown at two camera angels (bottom left and right). EE4-62 MLCV

  6. We attempt to map 2D image space to 3D pose space. There is inherent ambiguity in pose estimation (as an example in the above). EE4-62 MLCV

  7. Eye tracking can be tackled as a regression problem, where the input is an image I and the output is a eye location. EE4-62 MLCV

  8. Typical image processing steps: Given an image, a silhouette is segmented. A shape descriptor is applied to the silhouette to yield a finite dimensional vector. (Belongie and Malik, Matching with Shape Contexts, 2000) EE4-62 MLCV

  9. The output is a vector of m joint angles. EE4-62 MLCV

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  15. EE4-62 MLCV Gaussian Process

  16. EE462 MLCV Review of Gaussian density estimation(lecture 1,2) i.i.d. (independent identical distributed) We want to find the Gaussian parameters from the given data. The problem is to find the parameters by maximising the posterior probability

  17. EE462 MLCV Review of Gaussian density estimation(lecture 1,2) We do not have priors on the parameters and data, thus we maximise the (log) likelihood function instead. Maximum Likelihood (ML) vs Maximum A Posterior (MAP) solutions: P(X|Y)P(Y) (e.g. Gaussian Process) P(X|Y)

  18. EE462 MLCV Review of polynomial curve fitting (lecture 1,2) We want to fit a polynomial curve to given data pairs (x,t). = = where The objective ftn to minimise is

  19. EE4-62 MLCV Gaussian Processes

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  23. EE4-62 MLCV Gaussian Processes for Regression

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  31. Gaussian Process MatlabToolboxhttp://www.lce.hut.fi/research/mm/gpstuff/install.shtml(try demo_regression_robust.m, demo_regression1.m)

  32. EE4-62 MLCV Learning Hyperparameters

  33. EE4-62 MLCV Automatic Relevance Determination

  34. Back to the pose estimation problem

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  36. It’ll never work… • is multivalued • and live in high dimensions EE4-62 MLCV

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  43. GMM : Gaussian Mixture Model GPLVM : Gaussian Process Latent Variable Model EE4-62 MLCV

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