IS = { zkontrolovano 24 Jan 2014 },
  UPDATE  = { 2013-10-07 },
  author =	 {{\v S}{\'a}ra, Radim and Matou{\v s}ek, Martin and 
                  Franc, Vojt{\v e}ch },
  title =	 {{RANSACing} Optical Image Sequences for {GEO} and 
                  near-{GEO} Objects},
  year =	 {2013},
  pages =	 {924--933},
  booktitle =	 {Proceedings of the Advanced Maui Optical and Space
                  Surveillance Technologies Conference},
  publisher =	 {Maui Economic Development Board},
  address =	 {Kihei, US},
issn        = { 2152-4629 },
  isbn = {978-1-62993-522-5},
  book_pages =	 {974},
  month =	 {September},
  day =		 {10-13},
  venue =	 {Maui, USA},
  annote =	 {This paper describes statistical models and an
    efficient Monte-Carlo algorithm for detecting tracks of slowly
    moving objects in optical telescope imagery sequences. The
    algorithm is based on accurate robust image pre-registration with
    respect to the star background, hot/warm pixel suppression,
    extracting dense normalized local image features, pixelwise
    statistical event detection, segmentation of event maps to
    putative image primitives, and finding consistent track sequences
    composed of the image primitives. Good performance at low SNR and
    robustness of detection with respect to fast or slow-moving thin
    overhead clouds is achieved by an event detection model which
    requires collecting at least 10 images of a particular spatial
    direction. The method does not degrade due to an accumulation of
    acquisition artifacts if more images are available. The track
    sequence detection method is similar in spirit to LINE [Yanagisawa
    et al, T JPN SOC AERONAUT S 2012]. The detection is performed by
    the RANSAC robust method modified for a concurrent detection of a
    fixed number of tracks, followed by an acceptance test based on a
    maximum posterior probability classifier. The statistical model of
    an image primitive track is based on the consistence between the
    size and the inclination angle of the image primitive, its image
    motion velocity, and the sidereal velocity, together with a
    consistence in relative magnitude. The method does not presume any
    particular movements of the object, as long as its motion velocity
    is constant. It can detect tracks without any constraints on their
    angular direction or length. The detection does not require
    repeated image transformations (rotations etc.), which makes it
    computationally efficient. The detection time is linear in the
    number of input images and, unlike in the LINE proposal method,
    the number of RANSAC proposals is (theoretically) independent of
    the number of putative image primitives. The current (unoptimized)
    experimental implementation run several hours on a standard
    two-core CPU architecture. Reliable detection up to the magnitude
    of 16.5 has been obtained on a test sequence of over 5800 images
    from the 50\,cm TAOS telescope at Lulin Observatory, Taiwan. A
    comparison with the FPGA Image Stacking, which was the most
    successful method tested by [Yanagisawa et al, AMOS 2012] shows
    the proposed method is able to detect 62% more objects of
    magnitudes 11 -- 13.5, 38% more objects of magnitudes 13.5 --
    16.5, but only 33% of objects of magnitudes 16.5 -- 19. If
    optimized for speed, the proposed algorithm would be suitable for
    online detection, assuming an order of 10 or more running images
    are buffered. The algorithm is not suitable for fast object
    velocities at which the object typically enters/escapes the field
    of view during exposure.},
  keywords =	 {Orbital debris, GEO objects, detection},
  prestige =	 {international},
  authorship =	 {65-25-10},
  project =	 {HS MUMOIRE, GACR P103/12/1578},
  psurl =	 {[paper in on-line proceedings] },