The demand for automatic scene change detection has massively increased in the last decades due to its importance regarding safety and security issues. Although deep learning techniques have provided significant enhancements in the field, such methods must learn which object belongs to the foreground or background beforehand. In this article, we propose an approach that employs siamese U-Nets to address the task of change detection, such that the model learns to perform semantic segmentation using background reference frames only. Therefore, any object that comes up into the scene defines a change. The experimental results show the robustness of the proposed model over the well-known public dataset CDNet2014. Additionally, we also consider a private dataset called “PetrobrasROUTES,” which comprises obstruction or abandoned objects in escape routes in hazardous environments. Moreover, the experiments show that the proposed approach is more robust to noise and illumination changes.

中文翻译：

---

一种新的基于连体的场景变化检测方法，适用于危险环境中的障碍路线

由于其在安全和安保问题上的重要性，对自动场景变化检测的需求在过去几十年中大幅增加。尽管深度学习技术在该领域提供了显着的增强，但此类方法必须事先了解哪个对象属于前景或背景。在本文中，我们提出了一种使用 siamese U-Nets 来解决变化检测任务的方法，以便模型学习仅使用背景参考帧来执行语义分割。因此，任何出现在场景中的对象都会定义一个变化。实验结果表明，所提出的模型在众所周知的公共数据集 CDNet2014 上具有鲁棒性。此外，我们还考虑了一个名为“PetrobrasROUTES”的私有数据集，”，包括危险环境中逃生路线中的障碍物或遗弃物品。此外，实验表明，所提出的方法对噪声和光照变化更具鲁棒性。