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ICIP
2009
IEEE
13 years 2 months ago
Object tracking by bidirectional learning with feature selection
This paper proposes a new tracking algorithm which combines object and background information, via building object and background appearance models simultaneously by nonparametric...
Heng Wang, Xinwen Hou, Cheng-Lin Liu
ICPR
2008
IEEE
14 years 6 months ago
Human tracking based on Soft Decision Feature and online real boosting
Online Boosting is an effective incremental learning method which can update weak classifiers efficiently according to the object being trackedt. It is a promising technique for o...
Hironobu Fujiyoshi, Masato Kawade, Shihong Lao, Ta...
CVPR
2007
IEEE
14 years 7 months ago
Learning Features for Tracking
We treat tracking as a matching problem of detected keypoints between successive frames. The novelty of this paper is to learn classifier-based keypoint descriptions allowing to i...
Michael Grabner, Helmut Grabner, Horst Bischof
AMFG
2005
IEEE
218views Biometrics» more  AMFG 2005»
13 years 10 months ago
Online Feature Selection Using Mutual Information for Real-Time Multi-view Object Tracking
It has been shown that features can be selected adaptively for object tracking in changing environments [1]. We propose to use the variance of Mutual Information [2] for online fea...
Alex Po Leung, Shaogang Gong
ICIP
2009
IEEE
14 years 6 months ago
Online Subjective Feature Selection For Occlusion Management In Tracking Applications
Most of the state-of-the-art tracking algorithms are prone to error when dealing with occlusions, especially when the involved moving objects are hardly discernible in appearance....