Vision-based detection and tracking of aerial targets for UAV collision avoidance

Mejias, Luis, McNamara, Scott, Lai, John S., & Ford, Jason J. (2010) Vision-based detection and tracking of aerial targets for UAV collision avoidance. In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Taipei International Convention Center, Taipei.

View at publisher


Machine vision represents a particularly attractive solution for sensing and detecting potential collision-course targets due to the relatively low cost, size, weight, and power requirements of the sensors involved (as opposed to radar). This paper describes the development and evaluation of a vision-based collision detection algorithm suitable for fixed-wing aerial robotics. The system was evaluated using highly realistic vision data of the moments leading up to a collision. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400m to about 900m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning of between 8-10 seconds ahead of impact, which approaches the 12.5 second response time recommended for human pilots. We make use of the enormous potential of graphic processing units to achieve processing rates of 30Hz (for images of size 1024-by- 768). Currently, integration in the final platform is under way.

Impact and interest:

29 citations in Scopus
Search Google Scholar™
15 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

429 since deposited on 21 Jun 2010
45 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 32793
Item Type: Conference Paper
Refereed: Yes
Additional Information: Accepted version:14/06/2010
Keywords: UAV sense and avoid, Hidden Markov Models, Computer vision
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AEROSPACE ENGINEERING (090100)
Divisions: Current > Research Centres > Australian Research Centre for Aerospace Automation
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2010 [please consult the authors]
Deposited On: 21 Jun 2010 21:14
Last Modified: 23 Jun 2015 03:03

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page