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Selective Incremental Learning for Face Tracking Using Staggered Multi-Scale LBP
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 Title & Authors
Selective Incremental Learning for Face Tracking Using Staggered Multi-Scale LBP
Lee, Yonggeol; Choi, Sang-Il;
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 Abstract
The incremental learning method performs well in face face tracking. However, it has a drawback in that it is sensitive to the tracking error in the previous frame due to the environmental changes. In this paper, we propose a selective incremental learning method to track a face more reliably under various conditions. The proposed method is robust to illumination variation by using the LBP(Local Binary Pattern) features for each individual frame. We select patches to be used in incremental learning by using Staggered Multi-Scale LBP, which prevents the propagation of tracking errors occurred in the previous frame. The experimental results show that the proposed method improves the face tracking performance on the videos with environmental changes such as illumination variation.
 Keywords
Incremental Visual Tracking;Selective Incremental Learning;Local Binary Pattern;Staggered-Multi Scale LBP;
 Language
Korean
 Cited by
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