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» Learning with Local Drift Detection
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AVSS
2006
IEEE
15 years 5 months ago
An Online Discriminative Approach to Background Subtraction
We present a simple, principled approach to detecting foreground objects in video sequences in real-time. Our method is based on an on-line discriminative learning technique that ...
Li Cheng, Shaojun Wang, Dale Schuurmans, Terry Cae...
97
Voted
CVPR
2001
IEEE
16 years 1 months ago
Learning Representative Local Features for Face Detection
This paper describes a face detection approach via learning local features. The key idea is that local features, being manifested by a collection of pixels in a local region, are ...
Xiangrong Chen, Lie Gu, Stan Z. Li, HongJiang Zhan...
WACV
2012
IEEE
13 years 7 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
117
Voted
CVPR
2011
IEEE
14 years 3 months ago
Robust Tracking Using Local Sparse Appearance Model and K-Selection
Online learned tracking is widely used for it’s adaptive ability to handle appearance changes. However, it introduces potential drifting problems due to the accumulation of erro...
Baiyang Liu, junzhou Huang, Casimir Kulikowski, Li...
79
Voted
ICRA
2000
IEEE
111views Robotics» more  ICRA 2000»
15 years 4 months ago
Learning Globally Consistent Maps by Relaxation
Mobile robots require the ability to build their own maps to operate in unknown environments. A fundamental problem is that odometry-based dead reckoning cannot be used to assign ...
Tom Duckett, Stephen Marsland, Jonathan Shapiro