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Embedding Motion in Model-Based Stochastic Tracking

Publications associées (55)

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Motion Modeling: Can We Get Rid of Motion Capture?

Daniel Thalmann

For situations like crowd simulation, serious games, and VR-based training, flexible and spontaneous movements are extremely important. Motion models would be the best strategy to adopt, but unfortunately, they are very costly to develop and the results ar ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2008

Iterative rigid body transformation estimation for visual 3-D object tracking

Aude Billard, Micha Hersch

We present a novel yet simple 3D stereo vision tracking algorithm which computes the position and orientation of an object from the location of markers attached to the object. The novelty of this algorithm is that it does not assume that the markers are tr ...

2008

Local velocity-adapted motion events for spatio-temporal recognition

Barbara Caputo

In this paper we address the problem in motion recognition using event-based local motion representations. We assume that similar patterns of motion contain similar events with consistent motion across image sequences. Using this assumption, we formulate t ...

2007

Combination of video-based camera trackers using a dynamically adapted particle filter

Touradj Ebrahimi

This paper presents a video-based camera tracker that combines marker-based and feature point-based cues in a particle filter framework. The framework relies on their complementary performance. Marker-based trackers can robustly recover camera position and ...

2007

Dynamic visual attention: competitive versus motion priority scheme

Heinz Hügli, Alexandre Bur

De¯ned as attentive process in presence of visual sequences, dynamic visual attention responds to static and motion features as well. For a computer model, a straightforward way to integrate these features is to combine all features in a competitive scheme ...

2007

Motion models for robust 3D human body tracking

Raquel Urtasun

In this work, we propose new ways to learn pose and motion priors models and show that they can be used to increase the performance of 3D body tracking algorithms, resulting in very realistic motions under very challenging conditions. We first explored an ...

EPFL2006

Robust Infants Face Tracking with Active Appearance Model : a Mixed State Condensation Approach

Jean-Philippe Thiran, Matteo Sorci, Luigi Bagnato

This work addresses the problem of tracking, in real video sequences, the global motion of an infant face as well as the local motion of its inner features. This is a challenging task in Computer Vision field, because of the variability of facial appearanc ...

2006

A Video Database for Head Pose Tracking Evaluation

Jean-Marc Odobez, Silèye Oumar Ba

This document describes our work to provide a video database, of people in real situations with their head pose continuously annotated through time. The head poses were annotated using a magnetic 3d location and orientation tracker, the flock of bird. The ...

IDIAP2005

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 ...

2004

Motion likelihood and proposal modeling in Model-Based Stochastic Tracking

Daniel Gatica-Perez, Jean-Marc Odobez

Particle filtering is now established as one of the most popular methods for visual tracking. Within this framework, there are two important considerations. The first one refers to the generic assumption that the observations are temporally independent giv ...

IDIAP2004