Ashton Fagg

Queensland University of Technology, Commonwealth Scientific and Industrial Research Organisation

Papers

Total Citations

559

H-Index

About

Ashton Fagg is a computer vision researcher whose work has fundamentally advanced the field of visual object tracking, particularly through the lens of high-speed video analysis. His most significant contribution is the creation of the "Need for Speed" (NfS) dataset and benchmark, introduced in his landmark 2017 paper. This pioneering work established the first-ever higher frame rate video dataset for object tracking, comprising 100 real-world videos captured at 240 FPS—totaling 380,000 frames. By demonstrating that conventional tracking algorithms falter at higher frame rates, Fagg’s research opened a new frontier in temporal resolution for computer vision, enabling more robust and accurate tracking in dynamic, fast-moving scenarios. The primary paper on this work has accumulated over 524 citations, underscoring its profound impact on the community. Fagg’s contributions have not only provided a critical resource for benchmarking but have also inspired subsequent research into high-speed perception systems, with applications ranging from autonomous driving to sports analytics. His work remains a cornerstone for researchers seeking to push the boundaries of real-time visual understanding.