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Online boosting is an effective incremental learning method which can update weak classifiers efficiently according to the object being tracked. It is a promising technique for online object tracking to adapt to the appearance variations of objects during tracking process. However, proposed online-boosting based tracking methods update and select weak classifiers from fixed the offline learned weak...
Linear discrimination analysis (LDA) is one of the most popular feature extraction and classifier design techniques. It maximizes the Fisher-ratio between between-class scatter matrix and within-class scatter matrix under a linear transformation, and the transformation is composed of the generalized eigenvectors of them. However, Fisher criterion itself can not decide the optimum norm of transformation...
Tracking object in low frame rate video or with abrupt motion poses two main difficulties which most conventional tracking methods can hardly handle: 1) poor motion continuity and increased search space; 2) fast appearance variation of target and more background clutter due to increased search space. In this paper, we address the problem from a view which integrates conventional tracking and detection,...
In recent years, boosting has been successfully applied to many practical problems in pattern recognition and computer vision fields such as object detection and tracking. As boosting is an offline training process with beforehand collected data, once learned, it cannot make use of any newly arriving ones. However, an offline boosted detector is to be exploited online and inevitably there must be...
Tracking object in low frame rate video or with abrupt motion poses two main difficulties which conventional tracking methods can barely handle: 1) poor motion continuity and increased search space; 2) fast appearance variation of target and more background clutter due to increased search space. In this paper, we address the problem from a view which integrates conventional tracking and detection,...
Robust visual tracking is always a challenging but yet intriguing problem owing to the appearance variability of target objects. In this paper we propose a novel method to handle large changes in appearance based on online real-value boosting, which is utilized to incrementally learn a strong classifier to distinguish between objects and their background. By incorporating online real boosting into...
Scale invariant feature transform (SIFT) proposed by Lowe has been widely and successfully applied to object detection and recognition. However, the representation ability of SIFT features in face recognition has rarely been investigated systematically. In this paper, we proposed to use the person-specific SIFT features and a simple non-statistical matching strategy combined with local and global...
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