Concept

Video tracking

Related publications (129)

Fast multi-view face tracking with pose estimation

Jean-Philippe Thiran, Javier Cruz Mota, Julien Meynet

In this paper, a fast and an effective multi-view face tracking algorithm with head pose estimation is introduced. For modeling the face pose we employ a tree of boosted classiﬁers built using either Haar-like ﬁlters or Gauss ﬁlters. A ﬁrst classiﬁer extra ...

2008

Fast multi-view face tracking with pose estimation

Jean-Philippe Thiran, Javier Cruz Mota, Julien Meynet

In this paper, a fast and effective multi-view face tracking algorithm with head pose estimation is introduced. For modeling the face pose, we employ a tree of boosted classifiers built using either Haar-like filters or Gauss filters. A first classifier ex ...

2008

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

Motion Segmentation and Its Applications to Depth Ordering and Frame Rate Up-Conversion

Engin Türetken

In this thesis, layered motion segmentation problem and its applications to 2.5D scene representation and frame interpolation are investigated. As an initial step towards a layered design, building blocks of a generic motion segmentation algorithm; motion ...

2008

Particle filter-based camera tracker fusing marker- and feature point-based cues

Touradj Ebrahimi, Yousri Abdeljaoued

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 performances. Marker-based trackers can robustly recover camera position an ...

2007

Target tracking using a joint acoustic video system

Volkan Cevher

In this paper, we present a particle filter that exploits multi modal information for robust target tracking. We demonstrate a Bayesian framework for combining acoustic and video information using a state space approach. A proposal strategy for joint acous ...

2007

Advances in top-down and bottom-up approaches to video-based camera tracking

David Marimon Sanjuan

Video-based camera tracking consists in trailing the three dimensional pose followed by a mobile camera using video as sole input. In order to estimate the pose of a camera with respect to a real scene, one or more three dimensional references are needed. ...

EPFL2007

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

Bridging the Gap between Detection and Tracking for 3D Monocular Video-Based Motion Capture

Pascal Fua, Vincent Lepetit, Andrea Fossati, Miodrag Dimitrijevic

We combine detection and tracking techniques to achieve robust 3-D motion recovery of people seen from arbitrary viewpoints by a single and potentially moving camera. We rely on detecting key postures, which can be done reliably, using a motion model to in ...

2007

Stability and Convergence of the Level Set Method in Computer Vision

Kunal Narayan Chaudhury

Several computer vision problems, like segmentation, tracking and shape modeling, are increasingly being solved using level set methodologies. But the critical issues of stability and convergence have always been neglected in most of the level set implemen ...

Elsevier2007

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.