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.
Simple online and realtime tracking
20162.2k citationsAlex Bewley, Zongyuan Ge et al.arXiv (Cornell University)profile →
DeepFruits: A Fruit Detection System Using Deep Neural Networks
2016807 citationsZongyuan Ge, Feras Dayoub et al.profile →
On the performance of ConvNet features for place recognition
2015368 citationsNiko Sünderhauf, Sareh Shirazi et al.QUT ePrints (Queensland University of Technology)profile →
This map shows the geographic impact of Ben Upcroft'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 Ben Upcroft with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ben Upcroft more than expected).
This network shows the impact of papers produced by Ben Upcroft. 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 Ben Upcroft. The network helps show where Ben Upcroft may publish in the future.
Co-authorship network of co-authors of Ben Upcroft
This figure shows the co-authorship network connecting the top 25 collaborators of Ben Upcroft.
A scholar is included among the top collaborators of Ben Upcroft 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 Ben Upcroft. Ben Upcroft is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bewley, Alex, Zongyuan Ge, Lionel Ott, Fábio Ramos, & Ben Upcroft. (2016). Simple online and realtime tracking. arXiv (Cornell University). 3464–3468.2225 indexed citations breakdown →
3.
Zhang, Fangyi, Jürgen Leitner, Ben Upcroft, & Peter Corke. (2016). Transferring Vision-based Robotic Reaching Skills from Simulation to Real World. arXiv (Cornell University).1 indexed citations
4.
Shirazi, Sareh, et al.. (2016). A robustness analysis of Deep Q Networks. QUT ePrints (Queensland University of Technology).1 indexed citations
5.
Suenderhauf, Niko, et al.. (2015). Multimodal deep autoencoders for control of a mobile robot. QUT ePrints (Queensland University of Technology).14 indexed citations
6.
Sünderhauf, Niko, Ben Upcroft, & Michael Milford. (2015). Continuous factor graphs for holistic scene understanding. QUT ePrints (Queensland University of Technology).2 indexed citations
7.
Glover, Arren, Edward Pepperell, Gordon Wyeth, Ben Upcroft, & Michael Milford. (2015). Repeatable condition-invariant visual odometry for sequence-based place recognition. QUT ePrints (Queensland University of Technology).1 indexed citations
8.
Schulz, Ruth, et al.. (2015). Constructing abstract maps from spatial descriptions for goal-directed exploration. QUT ePrints (Queensland University of Technology).1 indexed citations
9.
Leitner, Jürgen, et al.. (2015). Towards vision-based deep reinforcement learning for robotic motion control. QUT ePrints (Queensland University of Technology).9 indexed citations
10.
Suenderhauf, Niko, Chris McCool, Ben Upcroft, & Tristán Pérez. (2014). Fine-grained plant classification using convolutional neural networks for feature extraction. QUT ePrints (Queensland University of Technology). 756–762.23 indexed citations
Dayoub, Feras, et al.. (2013). One Robot, Eight Hours, and Twenty Four Thousand People. QUT ePrints (Queensland University of Technology).1 indexed citations
13.
Warren, Michael, Peter Corke, Oscar Pizarro, Stefan B. Williams, & Ben Upcroft. (2012). Visual sea-floor mapping from low overlap imagery using bi-objective bundle adjustment and constrained motion. QUT ePrints (Queensland University of Technology).6 indexed citations
14.
Milford, Michael, David McKinnon, Michael Warren, Gordon Wyeth, & Ben Upcroft. (2011). Feature-based visual odometry and featureless place recognition for SLAM in 2.5D environments. International Conference on Robotics and Automation. 183(9). 495–9.3 indexed citations
15.
He, Hu, et al.. (2011). Towards Automatic Object Segmentation with Sequential Multiple Views. QUT ePrints (Queensland University of Technology).4 indexed citations
16.
Warren, Michael, David McKinnon, Hu He, & Ben Upcroft. (2010). Unaided stereo vision based pose estimation. QUT ePrints (Queensland University of Technology).25 indexed citations
17.
He, Hu, David McKinnon, Michael Warren, & Ben Upcroft. (2010). Graphcut-based interactive segmentation using colour and depth cues. QUT ePrints (Queensland University of Technology).7 indexed citations
18.
Douillard, Bertrand, Ben Upcroft, Tobias Kaupp, Fábio Ramos, & Hugh Durrant‐Whyte. (2007). Bayesian filtering over compressed appearance states. QUT ePrints (Queensland University of Technology).1 indexed citations
19.
Upcroft, Ben, et al.. (2004). Fast re-parameterisation of Gaussian mixture models for robotics applications. QUT ePrints (Queensland University of Technology). 1–7.9 indexed citations
20.
Hensinger, W. K., Antoine Browaeys, N. R. Heckenberg, et al.. (2001). Dynamical Tunneling of Ultracold Atoms. Nature. 412.8 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.