Daniel Seichter

624 total citations · 1 hit paper
18 papers, 424 citations indexed

About

Daniel Seichter is a scholar working on Computer Vision and Pattern Recognition, Civil and Structural Engineering and Aerospace Engineering. According to data from OpenAlex, Daniel Seichter has authored 18 papers receiving a total of 424 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Vision and Pattern Recognition, 4 papers in Civil and Structural Engineering and 4 papers in Aerospace Engineering. Recurrent topics in Daniel Seichter's work include Video Surveillance and Tracking Methods (7 papers), Human Pose and Action Recognition (6 papers) and Advanced Neural Network Applications (6 papers). Daniel Seichter is often cited by papers focused on Video Surveillance and Tracking Methods (7 papers), Human Pose and Action Recognition (6 papers) and Advanced Neural Network Applications (6 papers). Daniel Seichter collaborates with scholars based in Germany and Austria. Daniel Seichter's co-authors include Horst–Michael Groß, Markus Eisenbach, Ronny Stricker, Klaus Debes, Karl Amende, Steffen Müller, Matthias Hahn, Majon Muller, Denise Günther and Yvonne Steffen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Robotics and Autonomous Systems and International Journal of Social Robotics.

In The Last Decade

Daniel Seichter

14 papers receiving 411 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.

How to get pavement distress detection ready for deep learning? A systematic approach

2017 281 citations Markus Eisenbach, Ronny Stricker et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel Seichter Germany	8	308	104	79	49	49	18	424
Liangfu Ge China	10	292 0.9×	55 0.5×	104 1.3×	15 0.3×	50 1.0×	14	411
Mohamed Abdellatif Egypt	11	88 0.3×	69 0.7×	55 0.7×	47 1.0×	33 0.7×	46	328
Shanglian Zhou United States	10	291 0.9×	32 0.3×	97 1.2×	40 0.8×	28 0.6×	16	372
Fen Fang Singapore	9	108 0.4×	145 1.4×	34 0.4×	38 0.8×	50 1.0×	30	339
Xiaoxi Gong China	10	121 0.4×	189 1.8×	69 0.9×	29 0.6×	75 1.5×	15	381
Harriet Peel United Kingdom	7	196 0.6×	32 0.3×	64 0.8×	27 0.6×	29 0.6×	9	300
Wensheng Su China	10	184 0.6×	58 0.6×	278 3.5×	18 0.4×	43 0.9×	19	549
Shengbin Zhuang China	6	81 0.3×	208 2.0×	115 1.5×	18 0.4×	45 0.9×	10	302
Ziemowit Dworakowski Poland	9	190 0.6×	59 0.6×	125 1.6×	24 0.5×	9 0.2×	33	324
Yongzhi Min China	8	112 0.4×	46 0.4×	96 1.2×	15 0.3×	71 1.4×	35	269

Countries citing papers authored by Daniel Seichter

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Seichter

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Seichter

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

All Works

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18 of 18 papers shown

1.

Steffen, Yvonne, Daniel Seichter, Andrea Scheidig, et al.. (2025). A Mobile Robotic Assistant to Improve Social Care Networking for Older and Frail People at Home: Main Results of the MORPHIA-Project. International Journal of Social Robotics. 17(9). 1647–1670.

2.

Eisenbach, Markus, et al.. (2024). Detection of Novel Objects without Fine-Tuning in Assembly Scenarios by Class-Agnostic Object Detection and Object Re-Identification. SHILAP Revista de lepidopterología. 5(3). 373–406.

3.

Seichter, Daniel, et al.. (2024). GraspTrack: Object and Grasp Pose Tracking for Arbitrary Objects. 3371–3378.

4.

Seichter, Daniel, et al.. (2023). PanopticNDT: Efficient and Robust Panoptic Mapping. 7233–7240. 4 indexed citations

5.

Seichter, Daniel, et al.. (2023). Efficient Multi-Task Scene Analysis with RGB-D Transformers. 1–10. 7 indexed citations

6.

Seichter, Daniel, et al.. (2022). Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments. 2022 International Joint Conference on Neural Networks (IJCNN). 1–10. 30 indexed citations

7.

Stricker, Ronny, et al.. (2021). Road Surface Segmentation - Pixel-Perfect Distress and Object Detection for Road Assessment. 1789–1796. 20 indexed citations

8.

Seichter, Daniel, et al.. (2020). Multi-Task Deep Learning for Depth-based Person Perception in Mobile Robotics. 10497–10504. 7 indexed citations

9.

Seichter, Daniel, et al.. (2020). Real-time person orientation estimation and tracking using colored point clouds. Robotics and Autonomous Systems. 135. 103665–103665. 2 indexed citations

10.

Müller, Steffen, Daniel Seichter, & Horst–Michael Groß. (2019). Cross-Talk Compensation in Low-Cost Resistive Pressure Matrix Sensors. 232–237. 6 indexed citations

11.

Eisenbach, Markus, et al.. (2019). Enhancing the Quality of Visual Road Condition Assessment by Deep Learning. 4 indexed citations

12.

Seichter, Daniel, et al.. (2019). Real-time Person Orientation Estimation using Colored Pointclouds. 1–7. 8 indexed citations

13.

Seichter, Daniel, et al.. (2019). Deep orientation: Fast and Robust Upper Body orientation Estimation for Mobile Robotic Applications. 441–448. 21 indexed citations

14.

Seichter, Daniel, Markus Eisenbach, Ronny Stricker, & Horst–Michael Groß. (2018). How to Improve Deep Learning based Pavement Distress Detection while Minimizing Human Effort. 63–70. 11 indexed citations

15.

Eisenbach, Markus, et al.. (2018). Speeding up Deep Neural Networks on the Jetson TX1. Common Library Network (Der Gemeinsame Bibliotheksverbund). 1 indexed citations

16.

Eisenbach, Markus, Ronny Stricker, Daniel Seichter, et al.. (2017). How to get pavement distress detection ready for deep learning? A systematic approach. 2039–2047. 281 indexed citations breakdown →

17.

Seichter, Daniel, et al.. (2016). AAC encoding detection and bitrate estimation using a convolutional neural network. Fraunhofer-Publica (Fraunhofer-Gesellschaft). abs 1311 2901. 2069–2073. 10 indexed citations

18.

Eisenbach, Markus, et al.. (2016). Cooperative multi-scale Convolutional Neural Networks for person detection. 267–276. 12 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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