Publication

A Rao-Blackwellized Mixed State Particle Filter for Head Pose Tracking

2005
Conference Papers

Abstract

This paper presents a Rao-Blackwellized mixed state particle filter for joint head tracking and pose estimation. Rao-Blackwellizing a particle filter consists of marginalizing some of the variables of the state space in order to exactly compute their posterior probability density function. Marginalizing variables reduces the dimension of the configuration space and makes the particle filter more efficient and requires a lower number of particles. Experiments were conducted on our head pose ground truth video database consisting of people engaged in meeting discussions. Results from these experiments demonstrated benefits of the Rao-Blackwellized particle filter model with fewer particles over the mixed state particle filter model.

Official source

https://infoscience.epfl.ch/entities/publication/ee4ff1da-ad05-4e0e-a371-a32ecb39a6f9

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2005
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/ee4ff1da-ad05-4e0e-a371-a32ecb39a6f9

About this result

Related concepts (32)

Particle filter

Particle filters, or sequential Monte Carlo methods, are a set of Monte Carlo algorithms used to find approximate solutions for filtering problems for nonlinear state-space systems, such as signal processing and Bayesian statistical inference. The filtering problem consists of estimating the internal states in dynamical systems when partial observations are made and random perturbations are present in the sensors as well as in the dynamical system.

Posterior probability

The posterior probability is a type of conditional probability that results from updating the prior probability with information summarized by the likelihood via an application of Bayes' rule. From an epistemological perspective, the posterior probability contains everything there is to know about an uncertain proposition (such as a scientific hypothesis, or parameter values), given prior knowledge and a mathematical model describing the observations available at a particular time.

Pose tracking

In virtual reality (VR) and augmented reality (AR), a pose tracking system detects the precise pose of head-mounted displays, controllers, other objects or body parts within Euclidean space. Pose tracking is often referred to as 6DOF tracking, for the six degrees of freedom in which the pose is often tracked. Pose tracking is sometimes referred to as positional tracking, but the two are separate. Pose tracking is different from positional tracking because pose tracking includes orientation whereas and positional tracking does not.