In this study, we investigate online Bayesian estimation of the measurement noise density of a given state space model using particle filters and Dirichlet process mixtures. Diri...
Monitoring and tracking human activities around restricted areas is an important issue in security and surveillance applications. The movement of different parts of the human body...
M. B. Guldogan, Fredrik Gustafsson, Umut Orguner, ...
— Particle filters have recently been applied with great success to mobile robot localization. This success is mostly due to their simplicity and their ability to represent arbi...
Location estimation is an important part of many ubiquitous computing systems. Particle filters are simulation-based probabilistic approximations which the robotics community has ...
Condensation is a widely-used tracking algorithm based on particle filters. Although some results have been achieved, it has several unpleasant behaviours. In this paper, we highl...
— Recently, Rao-Blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem. This technique applies a particle filter i...
Giorgio Grisetti, Gian Diego Tipaldi, Cyrill Stach...
This paper presents a novel approach to particle filtering which minimizes the total tracking distortion by considering dynamic variance of proposal density and adaptive number o...
This paper presents a novel approach to tracking people in multiple cameras. A target is tracked not only in each camera but also in the ground plane by individual particle filter...
Abstract— Particle filters have been applied with great success to various state estimation problems in robotics. However, particle filters often require extensive parameter tw...
— In order to be truly autonomous, robots that operate in natural, populated environments must have the ability to create a model of these unpredictable dynamic environments and ...