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» Adaptive Background Estimation for Object Tracking
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ICPR
2008
IEEE
15 years 10 months ago
Variational Maximum A Posteriori model similarity and dissimilarity matching
A new variational Maximum A Posteriori (MAP) contextual modeling approach is presented that minimizes the product of two ratios: (a) the ratio of the model distribution to the dis...
John Chiverton, Majid Mirmehdi, Xianghua Xie
CVPR
2004
IEEE
15 years 11 months ago
Incremental Density Approximation and Kernel-Based Bayesian Filtering for Object Tracking
Statistical density estimation techniques are used in many computer vision applications such as object tracking, background subtraction, motion estimation and segmentation. The pa...
Bohyung Han, Dorin Comaniciu, Ying Zhu, Larry S. D...
IBPRIA
2009
Springer
14 years 7 months ago
Real-Time Motion Detection for a Mobile Observer Using Multiple Kernel Tracking and Belief Propagation
We propose a novel statistical method for motion detection and background maintenance for a mobile observer. Our method is based on global motion estimation and statistical backgro...
Marc Vivet, Brais Martínez, Xavier Binefa
ICCV
2003
IEEE
15 years 11 months ago
On-Line Selection of Discriminative Tracking Features
This paper presents a method for evaluating multiple feature spaces while tracking, and for adjusting the set of features used to improve tracking performance. Our hypothesis is t...
Robert T. Collins, Yanxi Liu
CVPR
2007
IEEE
15 years 3 months ago
Target Tracking with Online Feature Selection in FLIR Imagery
We present a particle filter-based target tracking algorithm for FLIR imagery. A dual foreground and background model is proposed for target representation which supports robust ...
Vijay Venkataraman, Guoliang Fan, Xin Fan