James Ferryman

3.4k total citations · 1 hit paper
112 papers, 2.3k citations indexed

About

James Ferryman is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, James Ferryman has authored 112 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 84 papers in Computer Vision and Pattern Recognition, 43 papers in Artificial Intelligence and 23 papers in Signal Processing. Recurrent topics in James Ferryman's work include Video Surveillance and Tracking Methods (57 papers), Anomaly Detection Techniques and Applications (35 papers) and Human Pose and Action Recognition (25 papers). James Ferryman is often cited by papers focused on Video Surveillance and Tracking Methods (57 papers), Anomaly Detection Techniques and Applications (35 papers) and Human Pose and Action Recognition (25 papers). James Ferryman collaborates with scholars based in United Kingdom, Austria and France. James Ferryman's co-authors include Ali Shahrokni, Lulu Chen, Wei Hong, A. D. Worrall, Luis Patino, Stephen J. Maybank, David P. Young, Tahir Nawaz, Peter Wild and G. D. Sullivan and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Access and Sensors.

In The Last Decade

James Ferryman

111 papers receiving 2.2k citations

Hit Papers

A survey of human motion analysis using depth imagery 2013 2026 2017 2021 2013 50 100 150 200 250
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.

  1. A survey of human motion analysis using depth imagery
    2013 270 citations Wei Hong, James Ferryman et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James Ferryman United Kingdom 26 1.8k 661 289 213 194 112 2.3k
Keni Bernardin Germany 10 1.7k 0.9× 501 0.8× 113 0.4× 349 1.6× 152 0.8× 20 2.1k
Wongun Choi United States 16 2.6k 1.4× 1.1k 1.6× 144 0.5× 421 2.0× 148 0.8× 21 3.3k
Bo Wu United States 21 2.6k 1.4× 876 1.3× 121 0.4× 255 1.2× 158 0.8× 62 2.9k
Shanghua Gao China 11 1.7k 0.9× 687 1.0× 183 0.6× 172 0.8× 160 0.8× 14 2.7k
Manohar Paluri United States 14 2.7k 1.4× 1.3k 1.9× 206 0.7× 164 0.8× 537 2.8× 16 3.4k
Hamed Pirsiavash United States 21 2.3k 1.3× 1.0k 1.5× 181 0.6× 269 1.3× 175 0.9× 54 2.8k
Huijing Zhao China 28 1.5k 0.8× 503 0.8× 188 0.7× 548 2.6× 116 0.6× 128 2.6k
Yongming Rao China 29 2.4k 1.3× 767 1.2× 164 0.6× 229 1.1× 137 0.7× 49 3.4k
Kaiwen Duan China 10 1.8k 1.0× 429 0.6× 119 0.4× 486 2.3× 122 0.6× 19 2.6k

Countries citing papers authored by James Ferryman

Since Specialization
Citations

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

Fields of papers citing papers by James Ferryman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James Ferryman

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

All Works

20 of 20 papers shown
1.
Liang, Huizhi, et al.. (2025). Review of Explainable Graph-Based Recommender Systems. ACM Computing Surveys. 58(6). 1–35.
2.
Liang, Huizhi, et al.. (2023). Scalable and explainable visually-aware recommender systems. Knowledge-Based Systems. 263. 110258–110258. 9 indexed citations
3.
Bouma, Henri, et al.. (2021). Authentication of travel and breeder documents. 13–13. 3 indexed citations
4.
Boyle, Jonathan, Tahir Nawaz, & James Ferryman. (2017). Deep trajectory representation-based clustering for motion pattern extraction in videos. 1–6. 2 indexed citations
5.
Nawaz, Tahir, et al.. (2017). A method for performance diagnosis and evaluation of video trackers. Signal Image and Video Processing. 11(7). 1287–1295. 4 indexed citations
6.
Nawaz, Tahir, Bernhard Rinner, & James Ferryman. (2016). User-centric, embedded vision-based human monitoring. CentAUR (University of Reading). 25–30. 7 indexed citations
7.
Hong, Wei, et al.. (2015). On evaluation of depth accuracy in consumer depth sensors. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9875. 98750A–98750A. 1 indexed citations
8.
Wild, Peter, et al.. (2015). On fusion for multispectral iris recognition. CentAUR (University of Reading). 31–37. 8 indexed citations
9.
Patino, Luis & James Ferryman. (2014). PETS 2014: Dataset and challenge. 355–360. 18 indexed citations
10.
Ferryman, James, et al.. (2014). Biologically-inspired robust motion segmentation using mutual information. Computer Vision and Image Understanding. 122. 47–64. 9 indexed citations
11.
Evans, Murray, et al.. (2013). Multicamera object detection and tracking with object size estimation. 30. 177–182. 11 indexed citations
12.
Ferryman, James, et al.. (2010). PETS2010: Dataset and Challenge. CentAUR (University of Reading). 143–150. 83 indexed citations
13.
Evans, Murray & James Ferryman. (2010). Surveillance Camera Calibration from Observations of a Pedestrian. CentAUR (University of Reading). 20. 64–71. 3 indexed citations
14.
Ferryman, James & Ali Shahrokni. (2009). PETS2009: Dataset and challenge. CentAUR (University of Reading). 1–6. 335 indexed citations
15.
Ferryman, James. (2009). Proceedings of the thirteenth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance. 32 indexed citations
16.
Ferryman, James, et al.. (2009). Robust background model for pixel based people counting using a single uncalibrated camera. CentAUR (University of Reading). 1–8. 15 indexed citations
17.
Evans, Murray & James Ferryman. (2006). Group stochastic search for object detection and tracking. CentAUR (University of Reading). 153–158. 2 indexed citations
18.
Borg, Mark G., et al.. (2005). An Integrated Vision System for Aircraft Activity Monitoring. SPIRE - Sciences Po Institutional REpository. 3 indexed citations
19.
Ferryman, James & A. D. Worrall. (1999). LEARNING 3D OBJECT-CENTRED APPEARANCE MODELS FOR TRACKING. 3 indexed citations
20.
Ferryman, James, A. D. Worrall, & Stephen J. Maybank. (1997). VEHICLE TRACKING WITH APPLICATIONS TO COLLISION ALERT. British Machine Vision Conference. 3 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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