641 citations · 1.5k across the 32 of their papers we have counts for
35 papers
4th Workshop on Maritime Computer Vision (MaCVi): Challenge Overview
Benjamin Kiefer, Jan Lukas Augustin, Jon Muhovič +52
The 4th Workshop on Maritime Computer Vision (MaCVi) is organized as part of CVPR 2026. This edition features five benchmark challenges with emphasis on both predictive accuracy an…
A New Dataset and a Distractor-Aware Architecture for Transparent Object Tracking
Alan Lukezic, Ziga Trojer, Jiri Matas +1
Performance of modern trackers degrades substantially on transparent objects compared to opaque objects. This is largely due to two distinct reasons. Transparent objects are unique…
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024
Benjamin Kiefer, Lojze Žust, Matej Kristan +46
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). Three challenge…
Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation
Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj
RGB-based surface anomaly detection methods have advanced significantly. However, certain surface anomalies remain practically invisible in RGB alone, necessitating the incorporati…
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark
Lojze Žust, Janez Perš, Matej Kristan
The progress in maritime obstacle detection is hindered by the lack of a diverse dataset that adequately captures the complexity of general maritime environments. We present the fi…
eWaSR -- an embedded-compute-ready maritime obstacle detection network
Matija Teršek, Lojze Žust, Matej Kristan
Maritime obstacle detection is critical for safe navigation of autonomous surface vehicles (ASVs). While the accuracy of image-based detection methods has advanced substantially, t…
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
Benjamin Kiefer, Matej Kristan, Janez Perš +70
The 1 Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and…
A Low-Shot Object Counting Network With Iterative Prototype Adaptation
Nikola Djukic, Alan Lukezic, Vitjan Zavrtanik +1
We consider low-shot counting of arbitrary semantic categories in the image using only few annotated exemplars (few-shot) or no exemplars (no-shot). The standard few-shot pipeline…
Trans2k: Unlocking the Power of Deep Models for Transparent Object Tracking
Alan Lukezic, Ziga Trojer, Jiri Matas +1
Visual object tracking has focused predominantly on opaque objects, while transparent object tracking received very little attention. Motivated by the uniqueness of transparent obj…
DSR -- A dual subspace re-projection network for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj
The state-of-the-art in discriminative unsupervised surface anomaly detection relies on external datasets for synthesizing anomaly-augmented training images. Such approaches are pr…
Learning with Weak Annotations for Robust Maritime Obstacle Detection
Lojze Žust, Matej Kristan
Robust maritime obstacle detection is critical for safe navigation of autonomous boats and timely collision avoidance. The current state-of-the-art is based on deep segmentation ne…
Temporal Context for Robust Maritime Obstacle Detection
Lojze Žust, Matej Kristan
Robust maritime obstacle detection is essential for fully autonomous unmanned surface vehicles (USVs). The currently widely adopted segmentation-based obstacle detection methods ar…
A Discriminative Single-Shot Segmentation Network for Visual Object Tracking
Alan Lukežič, Jiří Matas, Matej Kristan
Template-based discriminative trackers are currently the dominant tracking paradigm due to their robustness, but are restricted to bounding box tracking and a limited range of tran…
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj
Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. Recent surface anomaly detection methods rely on generative m…
Learning Maritime Obstacle Detection from Weak Annotations by Scaffolding
Lojze Žust, Matej Kristan
Coastal water autonomous boats rely on robust perception methods for obstacle detection and timely collision avoidance. The current state-of-the-art is based on deep segmentation n…
MODS -- A USV-oriented object detection and obstacle segmentation benchmark
Borja Bovcon, Jon Muhovič, Duško Vranac +3
Small-sized unmanned surface vehicles (USV) are coastal water devices with a broad range of applications such as environmental control and surveillance. A crucial capability for au…
A water-obstacle separation and refinement network for unmanned surface vehicles
Borja Bovcon, Matej Kristan
Obstacle detection by semantic segmentation shows a great promise for autonomous navigation in unmanned surface vehicles (USV). However, existing methods suffer from poor estimatio…
DAL -- A Deep Depth-aware Long-term Tracker
Yanlin Qian, Alan Lukežič, Matej Kristan +2
The best RGBD trackers provide high accuracy but are slow to run. On the other hand, the best RGB trackers are fast but clearly inferior on the RGBD datasets. In this work, we prop…
D3S -- A Discriminative Single Shot Segmentation Tracker
Alan Lukežič, Jiří Matas, Matej Kristan
Template-based discriminative trackers are currently the dominant tracking paradigm due to their robustness, but are restricted to bounding box tracking and a limited range of tran…
CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark
Alan Lukežič, Ugur Kart, Jani Käpylä +4
A long-term visual object tracking performance evaluation methodology and a benchmark are proposed. Performance measures are designed by following a long-term tracking definition t…
Performance Evaluation Methodology for Long-Term Visual Object Tracking
Alan Lukežič, Luka Čehovin Zajc, Tomáš Vojíř +2
A long-term visual object tracking performance evaluation methodology and a benchmark are proposed. Performance measures are designed by following a long-term tracking definition t…
Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks
Domen Tabernik, Matej Kristan, Aleš Leonardis
Convolutional neural networks excel in a number of computer vision tasks. One of their most crucial architectural elements is the effective receptive field size, that has to be man…
Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters
Ugur Kart, Alan Lukezic, Matej Kristan +2
Standard RGB-D trackers treat the target as an inherently 2D structure, which makes modelling appearance changes related even to simple out-of-plane rotation highly challenging. We…
Now you see me: evaluating performance in long-term visual tracking
Alan Lukežič, Luka Čehovin Zajc, Tomáš Vojíř +2
We propose a new long-term tracking performance evaluation methodology and present a new challenging dataset of carefully selected sequences with many target disappearances. We per…