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Works61 from public data
- 1,124
- The Visual Object Tracking VOT2015 Challenge Results902
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.
- The Sixth Visual Object Tracking VOT2018 Challenge Results804
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 state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
- A Novel Performance Evaluation Methodology for Single-Target Trackers667
The requirements are the basis of a new evaluation methodology that aims at a simple and easily interpretable tracker comparison and a fully-annotated dataset with per-frame annotations with several visual attributes, which is the largest benchmark to date.
- The Visual Object Tracking VOT2014 Challenge Results588
The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and introduces a new dataset with full annotation of targets by rotated bounding boxes and per-frame attribute.
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- The Eighth Visual Object Tracking VOT2020 Challenge Results350
A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in theVDT challenges.
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- Visual Object Tracking Performance Measures Revisited221
This paper revisit the popular performance measures and tracker performance visualizations and analyzes them theoretically and experimentally to show that several measures are equivalent from the point of information they provide for tracker comparison and, crucially, that some are more brittle than the others.
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- The Ninth Visual Object Tracking VOT2021 Challenge Results127
The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative; results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years.
- Deformable Parts Correlation Filters for Robust Visual Tracking113
A new formulation of the constellation model with correlation filters that treats the geometric and visual constraints within a single convex cost function and derive a highly efficient optimization for maximum a posteriori inference of a fully connected constellation is presented.
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- FuCoLoT – A Fully-Correlational Long-Term Tracker77
The proposed FuCoLoT exploits the novel DCF constrained filter learning method to design a detector that is able to re-detect the target in the whole image efficiently and has an order of magnitude smaller memory footprint than its best-performing competitors and runs at 15 fps in a single CPU thread.
- The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results66
A significant general improvement of results has been observed, which partly compensate for the more difficult sequences introduced into the dataset in VOT-TIR2016.
- Empirical evaluation of feature selection methods in classification51
An empirical evaluation of five feature selection methods shows that ReliefF and random forest enabled the classifiers to achieve the highest increase in classification accuracy on the average while reducing the number of unnecessary attributes.
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- Beyond Standard Benchmarks: Parameterizing Performance Evaluation in Visual Object Tracking23
This paper presents an approach that utilizes omnidirectional videos to generate realistic, consistently annotated, short-term tracking scenarios with exactly parameterized motion patterns, and provides an in-depth analysis of major tracking paradigms which is complementary to the standard benchmarks.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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