Ensemble learning-based method for maritime background subtraction in open sea environments

Abstract

Maritime autonomous surface ships employ computer vision to detect open sea environments for various applications, including safety surveillance, collision prevention, and autonomous navigation, all of which have grown increasingly important as maritime traffic has increased and automated systems have become more prevalent. Background subtraction (BS) may be used for real-time change detection in unseen contexts to obtain foreground information of interest without prior knowledge and training. However, because marine settings are dynamic, classic BS methods are vulnerable to noise (e.g., reflections and ship wakes) on the ocean surface. As a result, developing effective background models is challenging, resulting in considerable false foreground information. This study proposes a novel maritime BS method based on ensemble learning theory that integrates heterogeneous BS methods. Maritime noise filtering is also included to increase the background model’s ability to cope with complex marine situations. Experimental comparisons conducted on the Maritime BS Benchmark dataset showed that the proposed method had the highest real-time detection accuracy. The proposed method may also be used to improve autonomous ships’ situational awareness abilities in open waters, enhancing maritime transportation security.