Authors:
Vojtěch Cvrček
and
Radim Šára
Affiliation:
Department of Cybernetics, Czech Technical University in Prague and Czech Republic
Keyword(s):
Space Debris, Object Detection, Bayesian Model Selection, Image Analysis and Understanding.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Segmentation and Grouping
;
Shape Representation and Matching
Abstract:
Fast-moving celestial objects, like near-Earth objects (NEOs), orbiting space debris, or meteors, appear as streaks superimposed over the star background in images taken by an optical telescope at long exposures. As the apparent magnitude of the object increases (the object becomes fainter), its detection becomes progressively harder. We discuss a statistical procedure that makes a binary decision on the presence/absence of a streak in the image which is called streak certification. The certification is based purely on a single input image and a public star catalog, using a minimalistic statistical model. Certification accuracy greater than 90% for streaks of arbitrary orientation, longer than 500 pixels, and the signal-to-background log-ratio is better than −10dB is achieved on the same dataset as in an earlier similar method, whose performance is thus exceeded, especially for close-to-horizontal streaks. We also show that the certification decision indicates detection failure well.