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Claim this profileWorks168 from public data
- DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection1,297
The proposed DRÆM method enables direct anomaly localization without the need for additional complicated post-processing of the network output and can be trained using simple and general anomaly simulations.
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- 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.
- Reconstruction by inpainting for visual anomaly detection769
The RIAD approach (RIAD) randomly removes partial image regions and reconstructs the image from partial inpaintings, thus addressing the drawbacks of auto-enocoding methods.
- 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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- 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.
- Multivariate online kernel density estimation with Gaussian kernels211
This document includes some detailed supplemental derivations used in the bandwidth estimation for the online Kernel Density Estimator which was proposed in the paper \Multivariate Online Kernel D density Estimation with Gaussian Kernels.
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- Fast Image-Based Obstacle Detection From Unmanned Surface Vehicles187
A new graphical model is proposed that affords a fast and continuous obstacle image-map estimation from a single video stream captured on board a USV, and outperforms the related approaches, while requiring a fraction of computational effort.
- Stereo obstacle detection for unmanned surface vehicles by IMU-assisted semantic segmentation166
A new obstacle detection algorithm for unmanned surface vehicles (USVs) is presented, which significantly outperforms the state of the art, with nearly 30% improvement in water-edge detection accuracy, an over 21% reduction of false positive rate, and an over 65% increase of true positive rate while its Matlab implementation runs in real-time.
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- A trajectory-based analysis of coordinated team activity in a basketball game160
A probabilistic play model is applied to the player-trajectory data in order to segment the play into game phases (offense, defense, time out) and the activity is recognized by comparing its semantic description with the descriptions of manually defined templates, stored in a database.
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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.
- A Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform117
A novel measure of camera focus based on the Bayes spectral entropy of an image spectrum, which outperformed the reference measures by exhibiting a wider working range and a smaller failure rate.
- 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.
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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