Particle filter (PF) based object tracking methods have been widely used in computer vision. However, traditional particle filter trackers cannot effectively distinguish the target...
Xin Sun, Hongxun Yao, Shengping Zhang, Shaohui Liu
Particle Filter methods are one of the dominant tracking paradigms due to its ability to handle non-gaussian processes, multimodality and temporal consistency. Traditionally, the e...
Abstract Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided wit...
The basic nonlinear filtering problem for dynamical systems is considered. Approximating the optimal filter estimate by particle filter methods has become perhaps the most common a...
An "inconsistent" particle filter produces--in a statistical sense--larger estimation errors than predicted by the model on which the filter is based. Two test variables ...
: Data assimilation is a method of combining an imperfect simulation model and a number of incomplete observation data. Sequential data assimilation is a data assimilation in which...
Abstract-- This paper describes an on-line algorithm for multirobot simultaneous localization and mapping (SLAM). We take as our starting point the single-robot Rao-Blackwellized p...
Particle filter and mean shift are two successful approaches taken in the pursuit of robust tracking. Both of them have their respective strengths and weaknesses. In this paper, w...
Solving the tracking of an articulated structure in a reasonable time is a complex task mainly due to the high dimensionality of the problem. A new optimization method, called Sto...
Matthieu Bray, Esther Koller-Meier, Luc J. Van Goo...