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» Human Tracking by Fast Mean Shift Mode Seeking
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JMM2
2006
141views more  JMM2 2006»
13 years 5 months ago
Human Tracking by Fast Mean Shift Mode Seeking
Change detection by background subtraction is a common approach to detect moving foreground. The resulting difference image is usually thresholded to obtain objects based on pixel ...
Csaba Beleznai, Bernhard Frühstück, Hors...
ICCV
2005
IEEE
13 years 10 months ago
Fast Global Kernel Density Mode Seeking with Application to Localisation and Tracking
We address the problem of seeking the global mode of a density function using the mean shift algorithm. Mean shift, like other gradient ascent optimisation methods, is susceptible...
Chunhua Shen, Michael J. Brooks, Anton van den Hen...
JUCS
2010
265views more  JUCS 2010»
13 years 3 months ago
A General Framework for Multi-Human Tracking using Kalman Filter and Fast Mean Shift Algorithms
: The task of reliable detection and tracking of multiple objects becomes highly complex for crowded scenarios. In this paper, a robust framework is presented for multi-Human track...
Ahmed Ali, Kenji Terada
CVPR
2011
IEEE
13 years 1 months ago
Nonparametric Density Estimation on A Graph: Learning Framework, Fast Approximation and Application in Image Segmentation
We present a novel framework for tree-structure embedded density estimation and its fast approximation for mode seeking. The proposed method could find diverse applications in co...
Zhiding Yu, Oscar Au, Ketan Tang
ICMCS
2007
IEEE
191views Multimedia» more  ICMCS 2007»
13 years 11 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