In autonomous-ship and maritime security surveillance operations involving electro-optical sensors, the first phase of foreground segmentation and change detection using background subtraction (BS) algorithms is crucial. However, it is also the most complex in terms of execution time. Despite the development of several BS algorithms, maritime foreground segmentation and change detection remain major challenges owing to the complex, unconstrained, and diverse nature of ocean scenarios. However, only a few studies have investigated the applications of BS algorithms in maritime environments, especially those involving boats in the open sea. This study compares BS methods involving use of a non-static electro-optical sensor in combination with visible-light and infrared cameras to identify the best method for use in open sea scenarios, especially from the viewpoint of avoiding piracy and armed robbery. Thirty-seven methods, ranging from simple temporal differencing to more sophisticated ones, were validated via extensive experiments and analyses using realistic maritime datasets and practical maritime applications. In addition, because most methods considered in this study were not previously evaluated at the pixel level on open sea datasets, this paper proposes an appropriate maritime BS benchmark, based on which the 37 methods were compared to compensate for their prior lack of detailed analyses. The experimental results indicate that BS algorithms of the multiple features category can better handle maritime challenges, thereby realizing higher accuracies when analyzing visible-light and thermal videos. The proposed evaluation, therefore, complements those reported previously. Consequently, the proposed study enables users to identify the most suitable BS algorithm for use in intelligent maritime transportation, maritime security surveillance systems, and autonomous ships in the open sea.

在涉及光电传感器的自治船舶和海上安全监视操作中，使用背景减法（BS）算法进行前景分割和变化检测的第一阶段至关重要。但是，就执行时间而言，它也是最复杂的。尽管开发了几种BS算法，但由于海洋情景的复杂，不受约束和多样化的性质，海上前景分割和变化检测仍然是主要挑战。但是，只有很少的研究调查了BS算法在海洋环境中的应用，尤其是涉及海上公船的环境。这项研究比较了BS方法，该方法涉及将非静态光电传感器与可见光和红外热像仪结合使用，以确定在海上场景中使用的最佳方法，特别是从避免盗版和武装抢劫的角度来看。通过使用实际海事数据集和实际海事应用程序进行的广泛实验和分析，从简单的时差到更复杂的37种方法得到了验证。此外，由于本研究中考虑的大多数方法以前并未在公海数据集的像素级别进行评估，因此本文提出了合适的海上BS基准，在此基础上比较了这37种方法，以弥补以前缺乏详细分析的方法。实验结果表明，多特征类别的BS算法可以更好地应对海事挑战，从而在分析可见光和热视频时实现更高的精度。因此，拟议的评估是对先前报告的补充。