b'Matej Kristan'

research

∙ 08/18/2023

LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark

The progress in maritime obstacle detection is hindered by the lack of a...

0 Lojze Žust, et al. ∙

research

∙ 04/21/2023

eWaSR – an embedded-compute-ready maritime obstacle detection network

Maritime obstacle detection is critical for safe navigation of autonomou...

0 Matija Teršek, et al. ∙

research

∙ 11/15/2022

A Low-Shot Object Counting Network With Iterative Prototype Adaptation

We consider low-shot counting of arbitrary semantic categories in the im...

0 Nikola Djukic, et al. ∙

research

∙ 10/07/2022

Trans2k: Unlocking the Power of Deep Models for Transparent Object Tracking

Visual object tracking has focused predominantly on opaque objects, whil...

0 Alan Lukežič, et al. ∙

research

∙ 08/02/2022

DSR – A dual subspace re-projection network for surface anomaly detection

The state-of-the-art in discriminative unsupervised surface anomaly dete...

0 Vitjan Zavrtanik, et al. ∙

research

∙ 06/27/2022

Learning with Weak Annotations for Robust Maritime Obstacle Detection

Robust maritime obstacle detection is crucial for safe navigation of aut...

4 Lojze Žust, et al. ∙

research

∙ 03/10/2022

Temporal Context for Robust Maritime Obstacle Detection

Robust maritime obstacle detection is essential for fully autonomous unm...

0 Lojze Žust, et al. ∙

research

∙ 12/22/2021

A Discriminative Single-Shot Segmentation Network for Visual Object Tracking

Template-based discriminative trackers are currently the dominant tracki...

5 Alan Lukežič, et al. ∙

research

∙ 08/17/2021

DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection

Visual surface anomaly detection aims to detect local image regions that...

11 Vitjan Zavrtanik, et al. ∙

research

∙ 08/01/2021

Learning Maritime Obstacle Detection from Weak Annotations by Scaffolding

Coastal water autonomous boats rely on robust perception methods for obs...

0 Lojze Žust, et al. ∙

research

∙ 05/05/2021

MODS – A USV-oriented object detection and obstacle segmentation benchmark

Small-sized unmanned surface vehicles (USV) are coastal water devices wi...

0 Borja Bovcon, et al. ∙

research

∙ 01/07/2020

A water-obstacle separation and refinement network for unmanned surface vehicles

Obstacle detection by semantic segmentation shows a great promise for au...

29 Borja Bovcon, et al. ∙

research

∙ 12/02/2019

DAL – A Deep Depth-aware Long-term Tracker

The best RGBD trackers provide high accuracy but are slow to run. On the...

0 Yanlin Qian, et al. ∙

research

∙ 11/20/2019

D3S – A Discriminative Single Shot Segmentation Tracker

Template-based discriminative trackers are currently the dominant tracki...

10 Alan Lukežič, et al. ∙

research

∙ 07/01/2019

CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark

A long-term visual object tracking performance evaluation methodology an...

0 Alan Lukežič, et al. ∙

research

∙ 06/19/2019

Performance Evaluation Methodology for Long-Term Visual Object Tracking

A long-term visual object tracking performance evaluation methodology an...

2 Alan Lukežič, et al. ∙

research

∙ 02/20/2019

Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks

Convolutional neural networks excel in a number of computer vision tasks...

0 Domen Tabernik, et al. ∙

research

∙ 11/27/2018

Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters

Standard RGB-D trackers treat the target as an inherently 2D structure, ...

0 Uğur Kart, et al. ∙

research

∙ 04/19/2018

Now you see me: evaluating performance in long-term visual tracking

We propose a new long-term tracking performance evaluation methodology a...

2 Alan Lukežič, et al. ∙

research

∙ 02/22/2018

Stereo obstacle detection for unmanned surface vehicles by IMU-assisted semantic segmentation

A new obstacle detection algorithm for unmanned surface vehicles (USVs) ...

0 Borja Bovcon, et al. ∙

research

∙ 11/30/2017

Spatially-Adaptive Filter Units for Deep Neural Networks

Classical deep convolutional networks increase receptive field size by e...

0 Domen Tabernik, et al. ∙

research

∙ 11/27/2017

FCLT - A Fully-Correlational Long-Term Tracker

We propose FCLT - a fully-correlational long-term tracker. The two main ...

0 Alan Lukežič, et al. ∙

research

∙ 12/01/2016

Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking

Object-to-camera motion produces a variety of apparent motion patterns t...

0 Luka Čehovin Zajc, et al. ∙

research

∙ 11/25/2016

Discriminative Correlation Filter with Channel and Spatial Reliability

Short-term tracking is an open and challenging problem for which discrim...

0 Alan Lukežič, et al. ∙

research

∙ 09/13/2016

Towards Deep Compositional Networks

Hierarchical feature learning based on convolutional neural networks (CN...

0 Domen Tabernik, et al. ∙

research

∙ 03/08/2016

A regularization-based approach for unsupervised image segmentation

We propose a novel unsupervised image segmentation algorithm, which aims...

0 Aleksandar Dimitriev, et al. ∙

research

∙ 03/06/2015

Fast image-based obstacle detection from unmanned surface vehicles

Obstacle detection plays an important role in unmanned surface vehicles ...

0 Matej Kristan, et al. ∙

research

∙ 03/04/2015

A Novel Performance Evaluation Methodology for Single-Target Trackers

This paper addresses the problem of single-target tracker performance ev...

0 Matej Kristan, et al. ∙

research

∙ 02/20/2015

Visual object tracking performance measures revisited

The problem of visual tracking evaluation is sporting a large variety of...

0 Luka Čehovin, et al. ∙

Matej Kristan

Featured Co-authors