Feature-based annealing particle filter for robust motion capture

This thesis presents a new annealing method for particle filtering aiming at body pose estimation. Particle filters are Monte Carlo methods commonly employed in non-linear and non-Gaussian Bayesian problems, such as the estimation of human dynamics. However, they are ine±cient in high-dimensional state spaces. Annealed particle filter copes with such spaces by introducing a layered stochastic search. Our algorithm aims at generalizing and enhancing the classical annealed particle filter. Diferent image features are exploited in a sequential importance sampling scheme to build better proposal distributions from likelihood. This technique, termed Feature-Based Annealing, is inferred from the required function properties in the annealing process and the properties of the weighting functions obtained with common image features in the field of body tracking. Comparative results between the proposed strategy and common annealed particle filter are shown to assess the robustness of the algorithm

Using Gaussian Process Annealing Particle Filter for 3D Human Tracking

Abstract. We present an approach for human body parts tracking in 3D with prelearned motion models using multiple cameras. Gaussian Process Annealing Particle Filter is proposed for tracking in order to reduce the dimensionality of the problem and to increase the tracker’s stability and robustness. Comparing with a regular annealed particle filter based tracker, we show that our algorithm can track better for low frame rate videos. We also show that our algorithm is capable of recovering after a temporal target loose.

Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems

We discuss theory and application of extended object tracking. This task is challenging as sensor noise prevents a correct association of the measurements to their sources on the object, the shape itself might be unknown a priori, and due to occlusion effects, only parts of the object are visible at a given time. We propose an approach to track the parameters of arbitrary objects, which provides new solutions to the above challenges, and marks a significant advance to the state of the art