1.8k citations · 4.1k across the 34 of their papers we have counts for
6 papers · 1 filter
Deep Motion Features for Visual Tracking
Susanna Gladh, Martin Danelljan, Fahad Shahbaz Khan +1
Robust visual tracking is a challenging computer vision problem, with many real-world applications. Most existing approaches employ hand-crafted appearance features, such as HOG or…
ECO: Efficient Convolution Operators for Tracking
Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan +1
In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever increasing trac…
Discriminative Scale Space Tracking
Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan +1
Accurate scale estimation of a target is a challenging research problem in visual object tracking. Most state-of-the-art methods employ an exhaustive scale search to estimate the t…
Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking
Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan +1
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.…
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
Martin Danelljan, Andreas Robinson, Fahad Shahbaz Khan +1
Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit availabl…
Learning Spatially Regularized Correlation Filters for Visual Tracking
Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan +1
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…