Publication

Tracking Articulated Bodies using Generalized Expectation Maximization

Pascal Fua, Andrea Fossati
2008
Conference Papers

Abstract

A generalized expectation maximization (GEM) algorithm is used to retrieve the pose of a person from a monocular video sequence shot with a moving camera. After embedding the set of possible poses in a low dimensional space using principal component analysis, the configuration that gives the best match to the input image is held as estimate for the current frame. This match is computed iterating GEM to assign edge pixels to the correct body part and to find the body pose that maximizes the likelihood of the assignments.

Official source

https://infoscience.epfl.ch/entities/publication/727a3d8b-3e6d-4649-9421-ae9fa738ca49

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Pascal Fua, Andrea Fossati
2008
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/727a3d8b-3e6d-4649-9421-ae9fa738ca49

About this result

Related concepts (27)

Principal component analysis

Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data.

Dimensionality reduction

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable (hard to control or deal with).

Kernel principal component analysis

In the field of multivariate statistics, kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are performed in a reproducing kernel Hilbert space. Recall that conventional PCA operates on zero-centered data; that is, where is one of the multivariate observations.

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