2019-04-23T10:57:33 n28032

ACRV Robotic Vision Challenge 1 Test Dev data

Viewed: 388

This is the test dev data for the first Australian Centre for Robotic Vision (ACRV) Robotic Vision Challenge.  It consists of 18 rendered video sequences, in which participants must detect objects, with both spatial and semantic uncertainty.

For more details, see https://competitions.codalab.org/competitions/21727.

The sequence contains synthetic data generated using Unreal Engine 4.  For more details on its generation, see the workshop paper: https://arxiv.org/abs/1903.07840.

Data files consist of 18 zip files, each containing a video sequence made up of multiple .png images.

Contact: contact@roboticvisionchallenge.org.

Geographical area of data collection

kmlPolyCoords
153.028415,-27.477357

Publications

Probabilistic Object Detection: Definition and Evaluation by David Hall, Feras Dayoub, John Skinner, Haoyang Zhang, Dimity Miller, Peter Corke, Gustavo Carneiro, Anelia Angelova, Niko Sünderhauf. 10 April 2019 https://arxiv.org/abs/1811.10800v3
The Probabilistic Object Detection Challenge by John Skinner, David Hall, Haoyang Zhang, Feras Dayoub, Niko Sunderhauf. 19 March 2019. https://arxiv.org/abs/1903.07840

Research areas

Simulation and Modelling
Computer Vision
Control Systems, Robotics and Automation

Cite this collection

ARC Centre of Excellence in Robotic Vision (2019): ACRV Robotic Vision Challenge 1 Test Dev data. Queensland University of Technology. (Image) https://doi.org/10.25912/5ca6b5be9b4e1

Related information

Further challenge information http://www.roboticvisionchallenge.org/

Licence


Creative Commons Attribution 4.0 (CC-BY)
http://creativecommons.org/licenses/by/4.0/

Copyright

© ARC Centre of Excellence in Robotic Vision, 2018.

Dates of data collection

From 07-03-2019 to 08-03-2019

Connections

Has association with
David Hall  (Researcher)
Dr Feras Dayoub  (Researcher)
Dr Haoyang Zhang  (Researcher)
John Skinner  (Researcher)
Niko Suenderhauf  (Researcher)

Contacts

Name: Dr Niko Sünderhauf
Phone: +61 7 3138 9971

Other

Date record created:
2019-04-04T10:04:18
Date record modified:
2019-04-23T10:57:33
Record status:
Published - Open Access