Title :
Moving target detection for sense and avoid using regional phase correlation
Author :
May, Kenneth ; Krouglicof, Nicholas
Author_Institution :
Fac. of Eng. & Appl. Sci., Memorial Univ. of Newfoundland, St. John´s, NL, Canada
Abstract :
This paper outlines a video-based method for detecting intruder aircraft to assist with sense and avoid for small, unmanned aerial vehicles (UAVs). A key consideration is that the algorithm is suitable for real-time implementation on field-programmable gate arrays (FPGAs). The method begins by estimating the motion in the scene using regional phase correlation, and then fitting the positional predictions obtained using these regional motion vectors to an affine model representing the effect of camera motion on the background imagery. A combination of metrics, including phase correlation peak height (a confidence measure) and the error between the position predicted by the affine model and that obtained using the measured phase correlation vector, is used to indicate regions of interest where moving targets are present. The ability of the algorithm to detect approaching aircraft is analyzed using a number of aerial video sequences with different encounter geometries.
Keywords :
autonomous aerial vehicles; correlation methods; field programmable gate arrays; image sequences; motion estimation; natural scenes; object detection; real-time systems; robot vision; video cameras; video signal processing; FPGA; UAV; aerial video sequences; affine model; background imagery; camera motion effect representation; confidence measure; field programmable gate arrays; moving target detection; phase correlation peak height; phase correlation vector; positional prediction fitting; real-time implementation; region of interest; regional motion vectors; regional phase correlation; scene motion estimation; unmanned aerial vehicles; video-based method intruder aircraft detection; Analytical models; Computational modeling; Discrete Fourier transforms; Image resolution; Robustness; Support vector machine classification; Surveillance;
Conference_Titel :
Robotics and Automation (ICRA), 2013 IEEE International Conference on
Conference_Location :
Karlsruhe
Print_ISBN :
978-1-4673-5641-1
DOI :
10.1109/ICRA.2013.6631256