Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Gerald SommerSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works297 from public data
- ECO: Efficient Convolution Operators for Tracking2,708
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.
- Accurate Scale Estimation for Robust Visual Tracking2,087
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.
- Learning Spatially Regularized Correlation Filters for Visual Tracking1,984
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.
- Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking1,770
Discriminative Correlation Filters have demonstrated excellent performance for visual object tracking and the key to their success is the ability to efficiently exploit available negative data.
- 1,512
- ATOM: Accurate Tracking by Overlap Maximization1,509
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.
- Discriminative Scale Space Tracking1,312
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.
- 975
- The monogenic signal924
A geometric phase interpretation that is based on the relation between the 1-D analytic signal and the 2-D monogenic signal established by the Radon (1986) transform is introduced.
- 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.
- 496
- Unveiling the Power of Deep Tracking484
This paper proposes a novel adaptive fusion approach that leverages the complementary properties of deep and shallow features to improve both robustness and accuracy in generic object tracking.
- RoMa: Robust Dense Feature Matching457
This work proposes a robust model, RoMa, leveraging frozen pretrained features from the foundation model DINOv2, and proposes a tailored transformer match decoder that predicts anchor probabilities, which enables it to express multimodality.
- 434
- Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking430
This work proposes a novel generic approach for alleviating the problem of corrupted training samples in tracking-by-detection frameworks by minimizing a single loss over both the target appearance model and the sample quality weights.
- Deep Projective 3D Semantic Segmentation366
This work states that semantic segmentation of 3D point clouds is a challenging problem with numerous real-world applications that requires voxelizations of the underlying point cloud data, leading to decreased spatial resolution and increased memory consumption.
- 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.
- 308
- DKM: Dense Kernelized Feature Matching for Geometry Estimation301
The proposed dense method, Dense Kernelized Feature Matching, sets a new state-of-the-art on multiple geometry estimation benchmarks, and achieves an improvement on MegaDepth-1500 of +4.9 and +8.9 AUC@5° compared to the best previous sparse method and dense method respectively.
- Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition226
This work proposes to use locally segmented contours combined with an implicit star-shaped object model as prototypes for the different sign classes in traffic sign recognition by using the correlation based matching scheme for Fourier descriptors and a fast cascaded match scheme for enforcing the spatial requirements.
- Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook224
This survey presents a systematic and thorough review of more than 90 DCFs and Siamese trackers, based on results in nine tracking benchmarks, and distinguishes and comprehensively review the shared as well as specific open research challenges in both these tracking paradigms.
- Confidence Propagation through CNNs for Guided Sparse Depth Regression223
This paper proposes an algebraically-constrained normalized convolution layer for CNNs with highly sparse input that has a smaller number of network parameters compared to related work and introduces the use of output confidence as an auxiliary information to improve the results.
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.