Jakub Sochor
- Computer Vision and Pattern Recognition top 2%
- Automotive Engineering top 5%
- Media Technology top 2%
- Aerospace Engineering
- Artificial Intelligence
- Topics
- Advanced Neural Network Applications (9 papers)Video Surveillance and Tracking Methods (8 papers)Vehicle License Plate Recognition (6 papers)
- Journals
- IEEE Transactions on Intelligent Transportation SystemsComputer Vision and Image UnderstandingarXiv (Cornell University)
In The Last Decade
Jakub Sochor
14 papers receiving 592 citations
Peers
Comparison fields: 5 of 52
- Computer Vision and Pattern Recognition 505
- Automotive Engineering 179
- Media Technology 175
- Aerospace Engineering 83
- Artificial Intelligence 75
Countries citing papers authored by Jakub Sochor
This map shows the geographic impact of Jakub Sochor'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 Jakub Sochor with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jakub Sochor more than expected).
Fields of papers citing papers by Jakub Sochor
This network shows the impact of papers produced by Jakub Sochor. 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 Jakub Sochor. The network helps show where Jakub Sochor may publish in the future.
Co-authorship network of co-authors of Jakub Sochor
This figure shows the co-authorship network connecting the top 25 collaborators of Jakub Sochor. A scholar is included among the top collaborators of Jakub Sochor 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 Jakub Sochor. Jakub Sochor is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 3 | |
| 3 | 103 | |
| 4 | 51 | |
| 5 | 3 | |
| 6 | 6 | |
| 7 | BoxCars: Improving Vehicle Fine-Grained Recognition using 3D Bounding Boxes in Traffic Surveillance. | 1 |
| 8 | BrnoCompSpeed: Review of Traffic Camera Calibration and Comprehensive Dataset for Monocular Speed Measurement. | 14 |
| 9 | 78 | |
| 10 | 72 | |
| 11 | 123 | |
| 12 | INCAST: Interactive Camera Streams for Surveillance Cams AR. | 0 |
| 13 | 0 | |
| 14 | 3 | |
| 15 | 71 | |
| 16 | 76 |
About Jakub Sochor
Jakub Sochor is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Automotive Engineering, having authored 16 papers that have together received 611 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (9 papers), Video Surveillance and Tracking Methods (8 papers) and Vehicle License Plate Recognition (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (505 citations), Media Technology (175 citations) and Automotive Engineering (179 citations). Jakub Sochor has collaborated with scholars based in Czechia and Russia. Frequent co-authors include Adam Herout, Roman Juránek, Jakub Špaňhel, Markéta Dubská, Jiří Havel, Pavel Zemčík and Petr Dobeš. Their work appears in journals such as IEEE Transactions on Intelligent Transportation Systems, Computer Vision and Image Understanding and arXiv (Cornell University).
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.