Search Results for author: Matej Kristan

Found 31 papers, 14 papers with code

1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

no code implementations24 Nov 2022 Benjamin Kiefer, Matej Kristan, Janez Perš, Lojze Žust, Fabio Poiesi, Fabio Augusto de Alcantara Andrade, Alexandre Bernardino, Matthew Dawkins, Jenni Raitoharju, Yitong Quan, Adem Atmaca, Timon Höfer, Qiming Zhang, Yufei Xu, Jing Zhang, DaCheng Tao, Lars Sommer, Raphael Spraul, Hangyue Zhao, Hongpu Zhang, Yanyun Zhao, Jan Lukas Augustin, Eui-ik Jeon, Impyeong Lee, Luca Zedda, Andrea Loddo, Cecilia Di Ruberto, Sagar Verma, Siddharth Gupta, Shishir Muralidhara, Niharika Hegde, Daitao Xing, Nikolaos Evangeliou, Anthony Tzes, Vojtěch Bartl, Jakub Špaňhel, Adam Herout, Neelanjan Bhowmik, Toby P. Breckon, Shivanand Kundargi, Tejas Anvekar, Chaitra Desai, Ramesh Ashok Tabib, Uma Mudengudi, Arpita Vats, Yang song, Delong Liu, Yonglin Li, Shuman Li, Chenhao Tan, Long Lan, Vladimir Somers, Christophe De Vleeschouwer, Alexandre Alahi, Hsiang-Wei Huang, Cheng-Yen Yang, Jenq-Neng Hwang, Pyong-Kun Kim, Kwangju Kim, Kyoungoh Lee, Shuai Jiang, Haiwen Li, Zheng Ziqiang, Tuan-Anh Vu, Hai Nguyen-Truong, Sai-Kit Yeung, Zhuang Jia, Sophia Yang, Chih-Chung Hsu, Xiu-Yu Hou, Yu-An Jhang, Simon Yang, Mau-Tsuen Yang

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection.

object-detection Object Detection +1

A Low-Shot Object Counting Network With Iterative Prototype Adaptation

no code implementations15 Nov 2022 Nikola Djukic, Alan Lukezic, Vitjan Zavrtanik, Matej Kristan

The standard few-shot pipeline follows extraction of appearance queries from exemplars and matching them with image features to infer the object counts.

Object Counting Object Localization

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

1 code implementation7 Oct 2022 Alan Lukezic, Ziga Trojer, Jiri Matas, Matej Kristan

Visual object tracking has focused predominantly on opaque objects, while transparent object tracking received very little attention.

Transparent objects Visual Object Tracking

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

1 code implementation2 Aug 2022 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.

Anomaly Detection

Learning with Weak Annotations for Robust Maritime Obstacle Detection

1 code implementation27 Jun 2022 Lojze Žust, Matej Kristan

Robust maritime obstacle detection is critical for safe navigation of autonomous boats and timely collision avoidance.

Domain Generalization

Temporal Context for Robust Maritime Obstacle Detection

1 code implementation10 Mar 2022 Lojze Žust, Matej Kristan

Robust maritime obstacle detection is essential for fully autonomous unmanned surface vehicles (USVs).

A Discriminative Single-Shot Segmentation Network for Visual Object Tracking

no code implementations22 Dec 2021 Alan Lukežič, Jiří Matas, Matej Kristan

D3S2 outperforms the leading segmentation tracker SiamMask on video object segmentation benchmarks and performs on par with top video object segmentation algorithms.

Semantic Segmentation Video Object Segmentation +2

DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection

3 code implementations17 Aug 2021 Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj

Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance.

Ranked #21 on Anomaly Detection on MVTec AD (using extra training data)

Anomaly Detection Defect Detection

Learning Maritime Obstacle Detection from Weak Annotations by Scaffolding

2 code implementations1 Aug 2021 Lojze Žust, Matej Kristan

Per-pixel ground truth labeling of such datasets, however, is labor-intensive and expensive.

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

2 code implementations5 May 2021 Borja Bovcon, Jon Muhovič, Duško Vranac, Dean Mozetič, Janez Perš, Matej Kristan

We propose a new obstacle segmentation performance evaluation protocol that reflects the detection accuracy in a way meaningful for practical USV navigation.

object-detection Object Detection

Reconstruction by Inpainting for Visual Anomaly Detection

2 code implementations17 Oct 2020 Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj

Visual anomaly detection addresses the problem of classification or localization of regions in an image that deviate from their normal appearance.

Anomaly Detection

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

3 code implementations7 Jan 2020 Borja Bovcon, Matej Kristan

Obstacle detection by semantic segmentation shows a great promise for autonomous navigation in unmanned surface vehicles (USV).

Autonomous Navigation Semantic Segmentation

DAL -- A Deep Depth-aware Long-term Tracker

no code implementations2 Dec 2019 Yanlin Qian, Alan Lukežič, Matej Kristan, Joni-Kristian Kämäräinen, Jiri Matas

In this work, we propose a deep depth-aware long-term tracker that achieves state-of-the-art RGBD tracking performance and is fast to run.

D3S -- A Discriminative Single Shot Segmentation Tracker

1 code implementation20 Nov 2019 Alan Lukežič, Jiří Matas, Matej Kristan

D3S outperforms the leading segmentation tracker SiamMask on video object segmentation benchmark and performs on par with top video object segmentation algorithms, while running an order of magnitude faster, close to real-time.

Semantic Segmentation Video Object Segmentation +2

Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters

no code implementations CVPR 2019 Ugur Kart, Alan Lukezic, Matej Kristan, Joni-Kristian Kamarainen, Jiri Matas

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.

3D Reconstruction Object Tracking

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

no code implementations19 Apr 2018 Alan Lukežič, Luka Čehovin Zajc, Tomáš Vojíř, Jiří Matas, Matej Kristan

We propose a new long-term tracking performance evaluation methodology and present a new challenging dataset of carefully selected sequences with many target disappearances.

Visual Tracking

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

no code implementations22 Feb 2018 Borja Bovcon, Rok Mandeljc, Janez Perš, Matej Kristan

The IMU readings are used to estimate the location of horizon line in the image, which automatically adjusts the priors in the probabilistic semantic segmentation model.

Edge Detection Semantic Segmentation

Spatially-Adaptive Filter Units for Deep Neural Networks

2 code implementations CVPR 2018 Domen Tabernik, Matej Kristan, Aleš Leonardis

Classical deep convolutional networks increase receptive field size by either gradual resolution reduction or application of hand-crafted dilated convolutions to prevent increase in the number of parameters.

Image Classification Semantic Segmentation

Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking

no code implementations ICCV 2017 Luka Čehovin Zajc, Alan Lukežič, Aleš Leonardis, Matej Kristan

Object-to-camera motion produces a variety of apparent motion patterns that significantly affect performance of short-term visual trackers.

Visual Object Tracking

Discriminative Correlation Filter with Channel and Spatial Reliability

4 code implementations CVPR 2017 Alan Lukežič, Tomáš Vojíř, Luka Čehovin, Jiří Matas, Matej Kristan

Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance.

Ranked #13 on Visual Object Tracking on VOT2017/18 (using extra training data)

Visual Object Tracking

Towards Deep Compositional Networks

no code implementations13 Sep 2016 Domen Tabernik, Matej Kristan, Jeremy L. Wyatt, Aleš Leonardis

We propose a novel analytic model of a basic unit in a layered hierarchical model with both explicit compositional structure and a well-defined discriminative cost function.

Deformable Parts Correlation Filters for Robust Visual Tracking

no code implementations12 May 2016 Alan Lukežič, Luka Čehovin, Matej Kristan

Deformable parts models show a great potential in tracking by principally addressing non-rigid object deformations and self occlusions, but according to recent benchmarks, they often lag behind the holistic approaches.

Object Localization Visual Tracking

A regularization-based approach for unsupervised image segmentation

no code implementations8 Mar 2016 Aleksandar Dimitriev, Matej Kristan

We propose a novel unsupervised image segmentation algorithm, which aims to segment an image into several coherent parts.

Image Segmentation Semantic Segmentation +2

Fast image-based obstacle detection from unmanned surface vehicles

no code implementations6 Mar 2015 Matej Kristan, Vildana Sulic, Stanislav Kovacic, Janez Pers

The algorithm is tested on a new, challenging, dataset for segmentation and obstacle detection in marine environments, which is the largest annotated dataset of its kind.

Visual object tracking performance measures revisited

no code implementations20 Feb 2015 Luka Čehovin, Aleš Leonardis, Matej Kristan

The problem of visual tracking evaluation is sporting a large variety of performance measures, and largely suffers from lack of consensus about which measures should be used in experiments.

Visual Object Tracking Visual Tracking

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