• Publications
  • Influence
Accurate Scale Estimation for Robust Visual Tracking
This paper presents a novel approach to robust scale estimation that can handle large scale variations in complex image sequences and shows promising results in terms of accuracy and efficiency. Expand
ECO: Efficient Convolution Operators for Tracking
This work revisit the core DCF formulation and introduces a factorized convolution operator, which drastically reduces the number of parameters in the model, and a compact generative model of the training sample distribution that significantly reduces memory and time complexity, while providing better diversity of samples. Expand
Learning Spatially Regularized Correlation Filters for Visual Tracking
The proposed SRDCF formulation allows the correlation filters to be learned on a significantly larger set of negative training samples, without corrupting the positive samples, and an optimization strategy is proposed, based on the iterative Gauss-Seidel method, for efficient online learning. Expand
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data bExpand
The Visual Object Tracking VOT2016 Challenge Results
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment. Expand
The Visual Object Tracking VOT2015 Challenge Results
The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and presents a new VOT 2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute. Expand
Convolutional Features for Correlation Filter Based Visual Tracking
The results suggest that activations from the first layer provide superior tracking performance compared to the deeper layers, and show that the convolutional features provide improved results compared to standard hand-crafted features. Expand
ATOM: Accurate Tracking by Overlap Maximization
This work proposes a novel tracking architecture, consisting of dedicated target estimation and classification components, and introduces a classification component that is trained online to guarantee high discriminative power in the presence of distractors. Expand
Adaptive Color Attributes for Real-Time Visual Tracking
The contribution of color in a tracking-by-detection framework is investigated and an adaptive low-dimensional variant of color attributes is proposed, suggesting that color attributes provides superior performance for visual tracking. Expand
Discriminative Scale Space Tracking
This paper proposes a novel scale adaptive tracking approach by learning separate discriminative correlation filters for translation and scale estimation in a tracking-by-detection framework that obtains the top rank in performance by outperforming 19 state-of-the-art trackers on OTB and 37 state of theart tracker on VOT2014. Expand