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ICMCS
2007
IEEE
191views Multimedia» more  ICMCS 2007»
14 years 1 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
AMDO
2008
Springer
13 years 9 months ago
Exploiting Structural Hierarchy in Articulated Objects Towards Robust Motion Capture
This paper presents a general analysis framework towards exploiting the underlying hierarchical and scalable structure of an articulated object for pose estimation and tracking. Th...
Cristian Canton-Ferrer, Josep R. Casas, Montse Par...
ICRA
2009
IEEE
175views Robotics» more  ICRA 2009»
13 years 5 months ago
A combination of particle filtering and deterministic approaches for multiple kernel tracking
Color-based tracking methods have proved to be efficient for their robustness qualities. The drawback of such global representation of an object is the lack of information on its s...
Céline Teuliere, Éric Marchand, Laur...
TCSV
2010
13 years 2 months ago
Object Tracking in Structured Environments for Video Surveillance Applications
Abstract--We present a novel tracking method for effectively tracking objects in structured environments. The tracking method finds applications in security surveillance, traffic m...
Junda Zhu, Yuanwei Lao, Yuan F. Zheng
CVPR
2005
IEEE
14 years 9 months ago
Kernel-Based Bayesian Filtering for Object Tracking
Particle filtering provides a general framework for propagating probability density functions in non-linear and non-Gaussian systems. However, the algorithm is based on a Monte Ca...
Bohyung Han, Ying Zhu, Dorin Comaniciu, Larry S. D...