Sciweavers

ICPR
2010
IEEE
14 years 6 months ago
On-Line Random Naive Bayes for Tracking
—Randomized learning methods (i.e., Forests or Ferns) have shown excellent capabilities for various computer vision applications. However, it was shown that the tree structure in...
Martin Godec, Christian Leistner, Amir Saffari, Ho...
AVSS
2007
IEEE
14 years 6 months ago
An audio-visual sensor fusion approach for feature based vehicle identification
Andreas Klausner, Allan Tengg, Christian Leistner,...
CVPR
2010
IEEE
14 years 8 months ago
Online Multiclass LPBoost
Online boosting is one of the most successful online learning algorithms in computer vision. While many challenging online learning problems are inherently multi-class, online boo...
Amir Saffari, Martin Godec, Thomas Pock, Christian...
CVPR
2010
IEEE
14 years 8 months ago
PROST: Parallel Robust Online Simple Tracking
Tracking-by-detection is increasingly popular in order to tackle the visual tracking problem. Existing adaptive methods suffer from the drifting problem, since they rely on selfup...
Jakob Santner, Christian Leistner, Amir Saffari, T...
ECCV
2008
Springer
15 years 2 months ago
Semi-supervised On-Line Boosting for Robust Tracking
Abstract. Recently, on-line adaptation of binary classifiers for tracking have been investigated. On-line learning allows for simple classifiers since only the current view of the ...
Helmut Grabner, Christian Leistner, Horst Bischof