Visual Tracking

ECO: Efficient Convolution Operators for Tracking

CVPR 2017. In this work we tackle the key causes behind the problems of computational complexity and over-fitting in advanced DCF trackers.

Learning Continuous Convolution Operators for Visual Tracking

ECCV 2016. In this work we develop a theoretical framework for discriminatevly learning a convolution operator in the continuous spatial domain. Our formulation enables a natural integration of multi-resolution deep feature maps. In addition, our continuous formulation is capable of accurate sub-pixel localization of the target.

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

CVPR 2016. In this work we propose a unified formulation for alleviating the problem of corrupted training samples in tracking-by-detection methods. This is achieved by minimizing a joint loss over both target appearance model and the training sample quality weights. Our approach is generic and can be integrated into any discriminative tracking framework.

Learning Spatially Regularized Correlation Filters for Visual Tracking

ICCV 2015. In this work we propose Spatially Regularized Discriminative Correlation Filters (SRDCF) for tracking. This effectively mitigates the unwanted boundary effects, which limits the performance of standard correlation based trackers.

Coloring Visual Tracking

CVPR 2014. Investigating how to incorporate color information into visual tracking.

Scale Estimation for Visual Tracking

BMVC 2014. Here we investigate the problem of accurate and fast scale estimation for visual tracking.

Senast uppdaterad: 2017-02-28