Edgar Seemann

2.1k citations
12 papers · 886 · 1 hit paper · h-index 8

Impact in

Papers in

Edgar Seemann

9 papers receiving 822 citations

Edgar Seemann's Hit Papers

Pedestrian Detection in Crowded Scenes 2005 · 610 citations
6100+7+14Years since publication200400600

Peers

Edgar Seemann
Comparison fields: 5 of 59
  • Computer Vision and Pattern Recognition 800
  • Human-Computer Interaction 77
  • Artificial Intelligence 269
  • Media Technology 52
  • Safety, Risk, Reliability and Quality 51
Replace Matthew Shreve with:
Matthew Shreve United States
Peiyun Hu United States
Shuyang Sun United Kingdom
David Duggins United States
Antonino Furnari Italy
Yanyu Xu China
Jun-Wei Hsieh Taiwan
Fernando Jaureguizar Spain
Antonio Greco Italy
Stefan Atev United States
Edgar Seemann relative to Matthew Shreve United States Matthew Shreve's profile →
Citations per field
00.5×2.7×
Matthew Shreve · 1×
Citations per year

Countries citing papers authored by Edgar Seemann

Since Specialization
Citations

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

Fields of papers citing papers by Edgar Seemann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 16 scholars most cited alongside Edgar Seemann, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Edgar Seemann Line = papers co-authored together Edgar Seemann links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1
Pedestrian Detection in Crowded Scenes
Hit paper breakdown →
2005610
2 200490
3 200655
4 200739
5 200537
6 200427
7 200314
8 201313
9
Teaching Mathematics in Online Courses - An Interactive Feedback and Assessment Tool
20141
10 20150
11 20140
12 20160

About Edgar Seemann

Edgar Seemann is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Human-Computer Interaction, Ocean Engineering and Cognitive Neuroscience, having authored 12 papers that have together received 886 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (5 papers), Advanced Image and Video Retrieval Techniques (3 papers), Advanced Neural Network Applications (3 papers), Intelligent Tutoring Systems and Adaptive Learning (2 papers), Human Pose and Action Recognition (2 papers), Hand Gesture Recognition Systems (1 paper), Automated Road and Building Extraction (1 paper) and Robotics and Sensor-Based Localization (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (800 citations), Human-Computer Interaction (77 citations), Artificial Intelligence (269 citations), Media Technology (52 citations) and Safety, Risk, Reliability and Quality (51 citations). Edgar Seemann has collaborated with scholars based in Germany and United States. Frequent co-authors include Bernt Schiele, Bastian Leibe, Kai Nickel, Mario Fritz, Rainer Stiefelhagen, Krystian Mikolajczyk, Jing Yu, Knut Möller, M. Bett and Brad A. Myers. Their work appears in journals such as Repository of Futwangen University of Applied Sciences (Furtwangen University) and Figshare.

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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