visionml/pytracking: papers linking to it

GitHub · 3,525 stars · 613 forks · Python · license GPL-3.0 · created 2019 · github.com/visionml/pytracking

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to visionml/pytracking (https://github.com/visionml/pytracking), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

5 papers in the tracked journals link to the GitHub repository visionml/pytracking in their Crossref records, the first in 2023; most of them (2) in 2023.
By category: AI (5); the journals with the most are Pattern Recognition (2), Neural Computing and Applications (1) and Applied Intelligence (1).

By year#

20232026

Papers linking to visionml/pytracking by publication year
Year	Papers
2023	2
2024	1
2025	1
2026	1

Journals#

Journals whose papers link to visionml/pytracking most
Journal	Papers
Pattern Recognition	2
Neural Computing and Applications	1
Applied Intelligence	1
Information Fusion	1

Papers linking to visionml/pytracking, most cited first
Published	Title	Journal	Authors	Citations	Link
2025-11-08	Revisiting color-event based tracking: A unified network, dataset, and metric	Pattern Recognition	Chuanming Tang, Xiao Wang, Ju Huang et al.	40	cites it
2024-05-11	CRTrack: Learning Correlation-Refine network for visual object tracking	Pattern Recognition	Wenkang Zhang, Fei Xie, Tianyang Xu et al.	21	cites it
2023-12-26	Self-supervised discriminative model prediction for visual tracking	Neural Computing and Applications	Di Yuan, Gu Geng, Xiu Shu et al.	6	cites it
2026-03-07	TSMamba: Multi-drone feature interaction via temporal-spatial mamba networks for aerial object tracking	Information Fusion	Han Wu, Hao Sun, Kui Liu et al.	6	cites it
2023-04-25	Multi-template global re-detection based on Gumbel-Softmax in long-term visual tracking	Applied Intelligence	Zhiqiang Hou, Jingyuan Ma, Wangsheng Yu et al.	3	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). visionml/pytracking: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-visionml-pytracking

@misc{scholar4ai-code-github-visionml-pytracking,
  author = {{Scholar4AI}},
  title = {visionml/pytracking: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-visionml-pytracking}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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