Georg Nebehay

2.5k total citations · 1 hit paper
9 papers, 628 citations indexed

About

Georg Nebehay is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Georg Nebehay has authored 9 papers receiving a total of 628 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 2 papers in Artificial Intelligence and 1 paper in Computer Networks and Communications. Recurrent topics in Georg Nebehay's work include Video Surveillance and Tracking Methods (7 papers), Advanced Vision and Imaging (3 papers) and Human Pose and Action Recognition (3 papers). Georg Nebehay is often cited by papers focused on Video Surveillance and Tracking Methods (7 papers), Advanced Vision and Imaging (3 papers) and Human Pose and Action Recognition (3 papers). Georg Nebehay collaborates with scholars based in Austria, United Kingdom and Australia. Georg Nebehay's co-authors include Roman Pflugfelder, Matej Kristan, Fatih Porikli, Gustavo J. Fernández, Luka Čehovin Zajc, Aleš Leonardis, Jiřı́ Matas, Bernhard Rinner, Lukas Esterle and Peter R. Lewis and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Computer and ANU Open Research (Australian National University).

In The Last Decade

Georg Nebehay

9 papers receiving 614 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.

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 — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Georg Nebehay Austria	5	563	163	85	58	52	9	628
Roman Pflugfelder Austria	9	663 1.2×	198 1.2×	92 1.1×	73 1.3×	83 1.6×	22	758
Weijian Ruan China	11	551 1.0×	105 0.6×	55 0.6×	85 1.5×	31 0.6×	34	647
Fangqiang Ding China	11	567 1.0×	376 2.3×	188 2.2×	85 1.5×	97 1.9×	20	679
Kan Wu China	6	368 0.7×	117 0.7×	31 0.4×	116 2.0×	59 1.1×	10	532
Fida El Baf France	4	569 1.0×	66 0.4×	62 0.7×	98 1.7×	63 1.2×	6	617
Amir Sadeghian United States	6	462 0.8×	125 0.8×	69 0.8×	109 1.9×	22 0.4×	8	548
R. Patil United States	3	676 1.2×	112 0.7×	62 0.7×	141 2.4×	107 2.1×	3	773
Ben Benfold United Kingdom	7	564 1.0×	80 0.5×	40 0.5×	215 3.7×	43 0.8×	8	638

Countries citing papers authored by Georg Nebehay

Since Specialization

Citations

This map shows the geographic impact of Georg Nebehay'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 Georg Nebehay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Georg Nebehay more than expected).

Fields of papers citing papers by Georg Nebehay

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Georg Nebehay. 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 Georg Nebehay. The network helps show where Georg Nebehay may publish in the future.

Co-authorship network of co-authors of Georg Nebehay

This figure shows the co-authorship network connecting the top 25 collaborators of Georg Nebehay. A scholar is included among the top collaborators of Georg Nebehay 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 Georg Nebehay. Georg Nebehay is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

1.

Kristan, Matej, Jiřı́ Matas, Aleš Leonardis, et al.. (2016). A Novel Performance Evaluation Methodology for Single-Target Trackers. IEEE Transactions on Pattern Analysis and Machine Intelligence. 38(11). 2137–2155. 385 indexed citations breakdown →

2.

Nebehay, Georg, et al.. (2016). CppMT (IDInteraction version). Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

3.

Rinner, Bernhard, et al.. (2015). Self-Aware and Self-Expressive Camera Networks. Computer. 48(7). 21–28. 24 indexed citations

4.

Nebehay, Georg & Roman Pflugfelder. (2015). Clustering of static-adaptive correspondences for deformable object tracking. 2784–2791. 111 indexed citations

5.

Kristan, Matej, Roman Pflugfelder, Aleš Leonardis, et al.. (2014). The VOT2013 challenge: overview and additional results. ANU Open Research (Australian National University). 10 indexed citations

6.

Nebehay, Georg & Roman Pflugfelder. (2014). Consensus-based matching and tracking of keypoints for object tracking. 89 indexed citations

7.

Dieber, Bernhard, Lukas Esterle, Bernhard Rinner, et al.. (2013). Ella: Middleware for multi-camera surveillance in heterogeneous visual sensor networks. 1–6. 4 indexed citations

8.

Nebehay, Georg & Roman Pflugfelder. (2013). TLM: Tracking-learning-matching of keypoints. 77. 1–6. 2 indexed citations

9.

Nebehay, Georg & Roman Pflugfelder. (2009). A self-calibration method for smart video cameras. 2353. 840–846. 2 indexed citations

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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