Detecting Moving Objects in Photometric Images Using 3D Hough Transform

Abstract

In response to the exponential growth of space debris, an increasing number of observation devices are being used for the observation of moving objects, such as space debris and asteroids, which require further improvements in data-processing capabilities for the detection of moving objects. In this study, we propose a rapid detection algorithm designed for detecting moving objects, leveraging the power of the 3D Hough transform. By the simulated image experiments, our results show that the detection rate increases with the number of continuous images when fully extracting objects. Based on this foundation, the object detection rate is at least 87% regardless of the object number in the image sequence when detecting objects from at least six continuous images. In the observed image experiments, we used source-extractor to extract sources. The results show the method can successfully detect objects with signal-to-noise ratio higher than three from sidereal tracking images and can identify asteroids from asteroid tracking images while maintaining a detection speed that meets the requirements for real-time processing.