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Feature point tracking combining the Interacting Multiple Model filter and an efficient assignment algorithm

Related publications (52)

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An Implicit Motion Likelihood for Tracking with Particle Filters

Daniel Gatica-Perez, Jean-Marc Odobez, Silèye Oumar Ba

Particle filters are now established as the most popular method for visual tracking. Within this framework, it is generally assumed that the data are temporally independent given the sequence of object states. In this paper, we argue that in general the da ...

IDIAP2003

An Implicit Motion Likelihood for Tracking with Particle Filters

Daniel Gatica-Perez, Jean-Marc Odobez, Silèye Oumar Ba

Springer Verlag2003

A Mixed-State I-Particle Filter for Multi-Camera Speaker Tracking

Daniel Gatica-Perez, Jean-Marc Odobez, Guillaume Lathoud

Tracking speakers in multi-party conversations represents an important step towards automatic analysis of meetings. In this paper, we present a probabilistic method for audio-visual (AV) speaker tracking in a multi-sensor meeting room. The algorithm fuses ...

IDIAP2003

A Mixed-State I-Particle Filter for Multi-Camera Speaker Tracking

Daniel Gatica-Perez, Jean-Marc Odobez, Guillaume Lathoud

2003

A Probabilistic Framework for Joint Head Tracking and Pose Estimation

Jean-Marc Odobez, Silèye Oumar Ba

Head Tracking and pose estimation are usually considered as two sequential and separate problems: pose is estimated on the head patch provided by a tracking module. However, precision in head pose estimation is dependent on tracking accuracy which itself c ...

IDIAP2003

How to choose the number of taps in a DFE?

Christina Fragouli

In this paper, we address the following problem. Given a fixed total number of feedforward and feedback filter taps in a decision feedback equalizer, what is the optimum number of taps of each filter and what is the optimum delay setting? We propose a simp ...

2002

A framework for state-space estimation with uncertain models

Ali H. Sayed

Develops a framework for state-space estimation when the parameters of the underlying linear model are subject to uncertainties. Compared with existing robust filters, the proposed filters perform regularization rather than deregularization. It is shown th ...

Institute of Electrical and Electronics Engineers2001

State estimation with uncertain parametric models

Ali H. Sayed

This paper develops robust estimation algorithms for state-space models that are subject to bounded parametric uncertainties. Compared with existing robust filters, the new filters perform data regulaarization rather than de-regularization and they do not ...

2000

Three-Dimensional Motion Estimation of Objects for Video Coding

Luciano Sbaiz

Three-dimensional (3-D) motion estimation is applied to the problem of motion compensation for video coding. We suppose that the video sequence consists of the perspective projections of a collection of rigid bodies which undergo a rototranslational motion ...

1998

Robustness of Gauss—Newton recursive methods: A deterministic feedback analysis

Ali H. Sayed

This paper provides a time-domain feedback analysis of the robustness performance of Gauss-Newton recursive methods that are often used in identification and control. These are recursive estimators that also involve updates of sample covariance matrices. S ...

Elsevier1996