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Embedding Motion in Model-Based Stochastic Tracking

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Embedding motion in model-based stochastic tracking

Daniel Gatica-Perez, Jean-Marc Odobez

Particle filtering (PF) is now established as one of the most popular methods for visual tracking. Within this framework, two assumptions are generally made. The first is that the data are temporally independent given the sequence of object states, and the ...

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3D Tracking for Gait Characterization and Recognition

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We propose an approach to gait analysis that relies on fitting 3--D temporal motion models to synchronized video sequences. These models allow us not only to track but also to recover motion parameters that can be used to recognize people and characterize ...

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3D human body tracking using deterministic temporal motion models

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There has been much effort invested in increasing the robustness of human body tracking by incorporating motion models. Most approaches are probabilistic in nature and seek to avoid becoming trapped into local minima by considering multiple hypotheses, whi ...

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To increase the reliability of existing human motion tracking algorithms, we propose a method for imposing limits on the underlying hierarchical joint structures in a way that is true to life. Unlike most existing approaches, we explicitly represent depend ...

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Sampling method reconstruction method and devices for sampling and/or reconstructing multidimensional signals

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Reconstruction method and devices for two-dimensional signals that are not bandlimited but have a parametric representation with a finite number of degrees of freedom. The signal is reconstructed from the samples obtained after a suitable filtering with a ...

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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