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» Tracking Discontinuous Motion Using Bayesian Inference
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ICMCS
2007
IEEE
191views Multimedia» more  ICMCS 2007»
15 years 8 months ago
Variable Number of "Informative" Particles for Object Tracking
Particle filter is a sequential Monte Carlo method for object tracking in a recursive Bayesian filtering framework. The efficiency and accuracy of the particle filter depends on t...
Yu Huang, Joan Llach
139
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CRV
2005
IEEE
208views Robotics» more  CRV 2005»
15 years 8 months ago
Topology Inference for a Vision-Based Sensor Network
In this paper we describe a technique to infer the topology and connectivity information of a network of cameras based on observed motion in the environment. While the technique c...
Dimitri Marinakis, Gregory Dudek
ICCV
2007
IEEE
15 years 8 months ago
Two-View Motion Segmentation by Mixtures of Dirichlet Process with Model Selection and Outlier Removal
This paper presents a novel motion segmentation algorithm on the basis of mixture of Dirichlet process (MDP) models, a kind of nonparametric Bayesian framework. In contrast to pre...
Yong-Dian Jian, Chu-Song Chen
CVPR
2007
IEEE
16 years 4 months ago
Bridging the Gap between Detection and Tracking for 3D Monocular Video-Based Motion Capture
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 d...
Andrea Fossati, Miodrag Dimitrijevic, Vincent Lepe...
ICML
2004
IEEE
16 years 3 months ago
Generative modeling for continuous non-linearly embedded visual inference
Many difficult visual perception problems, like 3D human motion estimation, can be formulated in terms of inference using complex generative models, defined over high-dimensional ...
Cristian Sminchisescu, Allan D. Jepson