Dr David Hall
Postdoctoral Research Fellow
Australian Centre for Robotic Vision (ACRV)
Queensland University of Technology
Fields of Expertise:
Staff Profile: profile
Arxiv Profile: arxiv home
Githib Page: https://github.com/david2611
Hi, I am David Hall, research fellow at the Queensland University of Technology (QUT) whose long-term goal is to see robots able to cope with the unpredictable real world.
I began this journey with my PhD on adaptable systems for autonomous weed species recognition as a part of the strategic investment in farm robotics (SIFR) team. Since April 2018 I have worked as part of the robotic vision challenge group within the Australian Centre of Robotic Vision (ACRV) and QUT Centre for Robotics designing challenges, benchmarks, and evaluation measures that assist emerging areas of robotic vision research.
As a part of the robotic vision challenge group, I have assisted in:
Defining the field of probabilistic object detection (PrOD)
Creating the probability-based detection quality (PDQ) evaluation measure
Developing a PrOD robotic vision challenge
Developing a scene understanding robotic vision challenge
Now that I have spent some time developing robotic vision challenges, I look forward to solving these problems and giving the world robust and adaptable robotic vision systems, whilst also keeping a keen eye on the progress of robotic vision analysis.
Recent 1st Author Publications
Title: Probabilstic Object Detection: Definition and Evaluation
Authors: David Hall, Feras Dayoub, John Skinner, Haoyang Zhang, Dimity Miller, Peter Corke, Gustavo Carneiro, Anelia Angelova, Niko Suenderhauf
Published in: 2020 IEEE Winter Conference on Applications of Computer Vision
Manuscript Download Link: WACV2020-arxiv
Probability-based Detection Quality
Title: A rapidly deployable classification system using visual data for the application of precision weed management.
Authors: David Hall, Feras Dayoub, Tristan Perez, Chris McCool
Published in: Computers and Electronics in Agriculture, Vol. 148
Manuscript Download Link: COMPAG2018_manuscript.pdf
Official Paper Link: