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» Online Selection of Tracking Features using AdaBoost
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ICPR
2002
IEEE
15 years 2 months ago
Better Features to Track by Estimating the Tracking Convergence Region
Reliably tracking key points and textured patches from frame to frame is the basic requirement for many bottomup computer vision algorithms. The problem of selecting the features ...
Zoran Zivkovic, Ferdinand van der Heijden
82
Voted
3DPVT
2004
IEEE
108views Visualization» more  3DPVT 2004»
15 years 1 months ago
Unsupervised Motion Classification by Means of Efficient Feature Selection and Tracking
This paper presents an efficient technique for human motion recognition; in particular, it is focused on labeling a movement as a walking or running displacement, which are the mo...
Angel Domingo Sappa, Niki Aifanti, Sotiris Malassi...
KDD
2012
ACM
178views Data Mining» more  KDD 2012»
12 years 12 months ago
Mining emerging patterns by streaming feature selection
Building an accurate emerging pattern classifier with a highdimensional dataset is a challenging issue. The problem becomes even more difficult if the whole feature space is unava...
Kui Yu, Wei Ding 0003, Dan A. Simovici, Xindong Wu
104
Voted
CVPR
2010
IEEE
15 years 6 months ago
Online Visual Vocabulary Pruning Using Pairwise Constraints
Given a pair of images represented using bag-of-visual words and a label corresponding to whether the images are “related”(must-link constraint) or “unrelated” (must not li...
Pavan Mallapragada, Rong Jin and Anil Jain
WACV
2012
IEEE
13 years 5 months ago
Online discriminative object tracking with local sparse representation
We propose an online algorithm based on local sparse representation for robust object tracking. Local image patches of a target object are represented by their sparse codes with a...
Qing Wang, Feng Chen, Wenli Xu, Ming-Hsuan Yang