Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Machine Learning
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Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Manifold Regularization: A New Learning Setting and Empirical Study
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Semi-supervised On-Line Boosting for Robust Tracking
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In recent years, online building an adaptive target appearance model has been investigated for robust visual tracking in a dynamic environment. However one inherent problem of adaptive appearance trackers is drift, a gradual adaptation of the tracker to non-targets. To alleviate this problem, we consider visual tracking in a novel online manifold regularization (Online-MR) setting where labeled and unlabeled data arrive sequentially in large volume for tracker update. A discriminative target appearance model based on Haar wavelet is learned at each frame and its output score is used as the input sample feature for Online-MR tracker learning. Such a combination of Online-MR semi-supervised learning and online appearance model adaptation results in a robust tracking scheme that can prevent the tracker from drifting while retain its adaptation to appearance changing. Experimental results demonstrate the effectiveness of the proposed method from comparisons with adaptive appearance model based trackers on several challenging video sequences.