End-to-End People Detection in Crowded Scenes
In The Last Decade
doi.org/10.1109/cvpr.2016.255 →Countries where authors are citing End-to-End People Detection in Crowded Scenes
This map shows the geographic impact of End-to-End People Detection in Crowded Scenes. 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 End-to-End People Detection in Crowded Scenes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites End-to-End People Detection in Crowded Scenes more than expected).
Fields of papers citing End-to-End People Detection in Crowded Scenes
This network shows the impact of End-to-End People Detection in Crowded Scenes. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the End-to-End People Detection in Crowded Scenes.
About End-to-End People Detection in Crowded Scenes
This paper, published in 2016, received 309 indexed citations . Written by Russell J. Stewart, Mykhaylo Andriluka and Andrew Y. Ng covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (258 citations), Artificial Intelligence (120 citations) and Aerospace Engineering (22 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.
This paper is also available at doi.org/10.1109/cvpr.2016.255.