Automatic 3D reconstruction of human poses from monocular images is a challenging
and popular topic in the computer vision community, which provides a wide range of
applications in multiple areas. Solutions for 3D pose estimation involve various learning
approaches, such as Support Vector Machines and Gaussian processes, but many
encounter difficulties in cluttered scenarios and require additional input data, such as
silhouettes, or controlled camera settings.
The project outlined consists of a framework that is capable of estimating the 3D
pose of a person from monocular image sequences without requiring background information
and which is robust to camera variations. The framework models the inherent
non-linearity found in human motion as it benefits from flexible learning approaches,
including a highly customizable 2D detector and a Gaussian process regressor trained
on specific action motions.
Results on the HumanEva benchmark show that the system outperforms previous
works obtaining a 70% decrease in average estimation error on identical datasets. Detailed
settings for experiments, test results and performance measures on 3D pose estimation
are provided.