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- 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.
- The Visual Object Tracking VOT2016 Challenge Results776
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.
- 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.
- D3S – A Discriminative Single Shot Segmentation Tracker299
Without per-dataset finetuning and trained only for segmentation as the primary output, D3S outperforms all trackers on VOT2016, VOT2018 and GOT-10k benchmarks and performs close to the state-of-the-artTrackers on the TrackingNet.
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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.
- A Low-Shot Object Counting Network With Iterative Prototype Adaptation112
A Low-shot Object Counting network with iterative prototype Adaptation (LOCA) is proposed, which outperforms all recent state-of-the-art methods on FSC147 benchmark by 20-30% in RMSE on one-shot and few-shot and achieves state-of-the-art on zero-shot scenarios, while demonstrating better generalization capabilities.
- A Distractor-Aware Memory for Visual Object Tracking with SAM2102
It is argued that a more sophisticated memory model is required, and a new distractor-aware memory model for SAM2 and an introspection-based update strategy that jointly addresses the segmentation accuracy as well as tracking robustness are proposed.
- Object Tracking by Reconstruction With View-Specific Discriminative Correlation Filters98
The proposed long-term RGB-D tracker called OTR – Object Tracking by Reconstruction performs online 3D target reconstruction to facilitate robust learning of a set of view-specific discriminative correlation filters (DCFs).
- CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark83
The CDTB dataset is the largest and most diverse dataset in RGB-D tracking, with an order of magnitude larger number of frames than related datasets, indicating a large gap between the two fields, which has not been previously detected by the prior benchmarks.
- 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.
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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.
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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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