Matej Kristan

11.6k total citations · 4 hit papers
89 papers, 4.0k citations indexed

About

Matej Kristan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Aerospace Engineering. According to data from OpenAlex, Matej Kristan has authored 89 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Computer Vision and Pattern Recognition, 27 papers in Artificial Intelligence and 17 papers in Aerospace Engineering. Recurrent topics in Matej Kristan's work include Video Surveillance and Tracking Methods (30 papers), Robotics and Sensor-Based Localization (15 papers) and Advanced Image and Video Retrieval Techniques (14 papers). Matej Kristan is often cited by papers focused on Video Surveillance and Tracking Methods (30 papers), Robotics and Sensor-Based Localization (15 papers) and Advanced Image and Video Retrieval Techniques (14 papers). Matej Kristan collaborates with scholars based in Slovenia, United Kingdom and Czechia. Matej Kristan's co-authors include Danijel Skočaj, Luka Čehovin Zajc, Vitjan Zavrtanik, Aleš Leonardis, Jiřı́ Matas, Janez Perš, Alan Lukežič, Stanislav Kovačič, Borja Bovcon and Matej Perše and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Sensors and Pattern Recognition.

In The Last Decade

Matej Kristan

85 papers receiving 3.9k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Discriminative Correlation Filter with Channel and Spatial Reliability

2017 778 citations Alan Lukežič, Luka Čehovin Zajc et al. profile →
DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection

2021 447 citations Vitjan Zavrtanik, Matej Kristan et al. 2021 IEEE/CVF International Conference on Computer Vision (ICCV) profile →
Reconstruction by inpainting for visual anomaly detection

2020 395 citations Vitjan Zavrtanik, Matej Kristan et al. profile →
A Novel Performance Evaluation Methodology for Single-Target Trackers

2016 385 citations Matej Kristan, Jiřı́ Matas et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →

Peers

Countries citing papers authored by Matej Kristan

Since Specialization

Citations

This map shows the geographic impact of Matej Kristan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matej Kristan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matej Kristan more than expected).

Fields of papers citing papers by Matej Kristan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matej Kristan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matej Kristan. The network helps show where Matej Kristan may publish in the future.

Co-authorship network of co-authors of Matej Kristan

This figure shows the co-authorship network connecting the top 25 collaborators of Matej Kristan. A scholar is included among the top collaborators of Matej Kristan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Matej Kristan. Matej Kristan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work	Indexed citations
1	A Distractor-Aware Memory for Visual Object Tracking with SAM2 2025 ·(unknown), Alan Lukežič, Matej Kristan	1
2	CRITER 1.0: a coarse reconstruction with iterative refinement network for sparse spatio-temporal satellite data 2025 ·Geoscientific model development ·(unknown), Vitjan Zavrtanik, Alexander Barth, Aïda Alvera Azcarate, Matjaž Ličer, Matej Kristan	0
3	Application of the HIDRA2 deep-learning model for sea level forecasting along the Estonian coast of the Baltic Sea 2025 ·Ocean science ·(unknown), Matjaž Ličer, (unknown), Matej Kristan, Ilja Maljutenko, Jüri Elken, Priidik Lagemaa, Rivo Uiboupin	0
4	Keep DRÆMing: Discriminative 3D anomaly detection through anomaly simulation 2024 ·Pattern Recognition Letters ·Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj	9
5	A New Dataset and a Distractor-Aware Architecture for Transparent Object Tracking 2024 ·International Journal of Computer Vision ·Alan Lukežič, (unknown), Jiřı́ Matas, Matej Kristan	0
6	HIDRA2: deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic 2023 ·Geoscientific model development ·(unknown), (unknown), Matej Kristan, Matjaž Ličer	9
7	eWaSR—An Embedded-Compute-Ready Maritime Obstacle Detection Network 2023 ·Sensors ·(unknown), Lojze Žust, Matej Kristan	7
8	Temporal Context for Robust Maritime Obstacle Detection 2022 ·2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ·Lojze Žust, Matej Kristan	11
9	DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection breakdown → 2021 ·2021 IEEE/CVF International Conference on Computer Vision (ICCV) ·Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj	447
10	HIDRA 1.0: deep-learning-based ensemble sea level forecasting in the northern Adriatic 2021 ·Geoscientific model development ·Lojze Žust, (unknown), Matej Kristan, Matjaž Ličer	17
11	Performance Evaluation Methodology for Long-Term Single-Object Tracking 2020 ·IEEE Transactions on Cybernetics ·Alan Lukežič, Luka Čehovin Zajc, (unknown), Jiřı́ Matas, Matej Kristan	16
12	Obstacle Detection for USVs by Joint Stereo-View Semantic Segmentation 2018 ·Borja Bovcon, Matej Kristan	24
13	2017 ·University of Birmingham Research Portal (University of Birmingham) ·Luka Čehovin Zajc, Alan Lukežič, Aleš Leonardis, Matej Kristan	14
14	A Novel Performance Evaluation Methodology for Single-Target Trackers breakdown → 2016 ·IEEE Transactions on Pattern Analysis and Machine Intelligence ·Matej Kristan, Jiřı́ Matas, Aleš Leonardis, (unknown), Roman Pflugfelder, Gustavo J. Fernández, Georg Nebehay, Fatih Porikli, Luka Čehovin Zajc	385
15	Dana36: A Multi-camera Image Dataset for Object Identification in Surveillance Scenarios 2012 ·(unknown), (unknown), Rok Mandeljc, Matej Kristan, Stanislav Kovačič	13
16	A Two-Stage Dynamic Model for Visual Tracking 2010 ·IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) ·Matej Kristan, Stanislav Kovačič, Aleš Leonardis, Janez Perš	47
17	Online kernel density estimation for interactive learning 2009 ·Image and Vision Computing ·Matej Kristan, Danijel Skočaj, Aleš Leonardis	29
18	AN ANALYSIS OF BASKETBALL PLAYERS' MOVEMENTS IN THE SLOVENIAN BASKETBALL LEAGUE PLAY-OFFS USING THE SAGIT TRACKING SYSTEM 2008 ·(unknown), Matej Perše, Matej Kristan	22
19	Observing Human Motion Using Far-Infrared (FLIR) Camera - Some Preliminary Studies 2004 ·Janez Perš, Matej Kristan, Matej Perše, (unknown)	2
20	Robust and efficient vision system for group of cooperating mobile robots with application to soccer robots 2004 ·ISA Transactions ·Gregor Klančar, Matej Kristan, Stanislav Kovačič, (unknown)	12

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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