Experimental evaluation of four feature detection methods for close range and distant airborne targets for Unmanned Aircraft Systems applications

DOIResolve DOI: http://doi.org/10.1109/ICUAS.2014.6842384
AuthorSearch for: ; Search for: ; Search for: ; Search for:
Proceedings title2014 International Conference on Unmanned Aircraft Systems, ICUAS 2014 - Conference Proceedings
Conference2014 International Conference on Unmanned Aircraft Systems, ICUAS 2014, 27 May 2014 through 30 May 2014, Orlando, FL
Article number6842384
Pages12671273; # of pages: 7
SubjectAircraft; Pixels; Unmanned aerial vehicles (UAV); Airborne target; Evaluation criteria; Experimental evaluation; Feature detection; Flight test data; Sense and avoid; Target feature; Unmanned aircraft system; Aircraft detection
AbstractFeature detection for Unmanned Aircraft Systems (UAS) sense and avoid scenarios is a crucial preliminary step for target detection. Its importance culminates when distant (pixel size) targets representing incoming aircraft are considered. This paper presents an experimental evaluation of four popular feature detection methods using flight test data and based on evaluation criteria such as first detection distance and percentage of frames with detected target features. Our results show that for close range targets all four methods have similar performance, while for distant (pixel-size) targets, the Shi and Tomasi method outperforms the other three methods (Harris-Stephens-Plessey, SUSAN and FAST).
Publication date
AffiliationNational Research Council Canada; Information and Communication Technologies; Aerospace
Peer reviewedYes
NPARC number21272938
Export citationExport as RIS
Report a correctionReport a correction
Record identifier888a9904-d1ed-43c5-a16d-04938df8fcb3
Record created2014-12-03
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)