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» People Tracking Using Hybrid Monte Carlo Filtering
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ICCV
2001
IEEE
14 years 6 months ago
People Tracking Using Hybrid Monte Carlo Filtering
Particle filters are used for hidden state estimation with nonlinear dynamical systems. The inference of 3-d human motion is a natural application, given the nonlinear dynamics of...
Kiam Choo, David J. Fleet
VMV
2008
168views Visualization» more  VMV 2008»
13 years 5 months ago
Integrating robust likelihoods with Monte-Carlo filters for multi-target tracking
In this paper, a dynamic multi-modal fusion scheme for tracking multiple targets with Monte-Carlo filters is presented, with the goal of achieving robustness by combining complime...
Giorgio Panin, Thorsten Röder, Alois Knoll
ICPR
2006
IEEE
14 years 5 months ago
An integrated Monte Carlo data association framework for multi-object tracking
We propose a sequential Monte Carlo data association algorithm based on a two-level computational framework for tracking varying number of interacting objects in dynamic scene. Fi...
Jianru Xue, Nanning Zheng, Xiaopin Zhong
ICPR
2002
IEEE
13 years 9 months ago
Stochastic Filtering for Motion Trajectory in Image Sequences Using a Monte Carlo Filter with Estimation of Hyper-Parameters
False matching due to errors in feature extraction and changes in illumination between frames may occur in feature tracking in image sequences. False matching leads to outliers in...
Naoyuki Ichimura
ICMCS
2007
IEEE
191views Multimedia» more  ICMCS 2007»
13 years 10 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