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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.
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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.
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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.
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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 b…
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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.
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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.
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Learning Discriminative Model Prediction for Tracking

An end-to-end tracking architecture, capable of fully exploiting both target and background appearance information for target model prediction, derived from a discriminative learning loss by designing a dedicated optimization process that is capable of predicting a powerful model in only a few iterations.
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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.
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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.
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The Sixth Visual Object Tracking VOT2018 Challenge Results

The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are…
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