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Gustav Hager

2 accepted papers

2016

Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking

CVPR 2016poster

Tracking-by-detection methods have demonstrated competitive performance in recent years. In these approaches, the tracking model heavily relies on the quality of the training set. Due to the limited amount of labeled training data, additional samples need to be extracted and labeled by the tracker i…

Cited by 515PDFScholar
2015

Learning Spatially Regularized Correlation Filters for Visual Tracking

ICCV 2015poster

Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target appearance model is crucial. Recently, discriminatively learned correlation filters (DCF) have been successfully applied…

Cited by 2649PDFScholar