Computer methods for 3D motion tracking in real-time

TL;DR

This thesis presents a real-time, marker-less 3D human motion tracking system using multi-camera setups, optimized rendering, and GPU-based computation to achieve full-body tracking without markers.

Contribution

It introduces novel real-time rendering and GPU-accelerated methods for 3D pose estimation using dynamic optimization and Bayesian filtering.

Findings

Achieved real-time full-body 3D motion tracking.

Developed GPU-based parallel methods for objective function computation.

Enabled marker-less motion tracking in multicamera systems.

Abstract

This thesis is devoted to marker-less 3D human motion tracking in calibrated and synchronized multicamera systems. Pose estimation is based on a 3D model, which is transformed into the image plane and then rendered. Owing to elaborated techniques the tracking of the full body has been achieved in real-time via dynamic optimization or dynamic Bayesian filtering. The objective function of a particle swarm optimization algorithm and the observation model of a particle filter are based on matching between the rendered 3D models in the required poses and image features representing the extracted person. In such an approach the main part of the computational overload is associated with the rendering of 3D models in hypothetical poses as well as determination of value of objective function. Effective methods for rendering of 3D models in real-time with support of OpenGL as well as parallel methods for determining the objective function on the GPU were developed. The elaborated solutions permit 3D tracking of full body motion in real-time.