RANSACing Optical Image Sequences for GEO and near-GEO Objects

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.