Background subtraction is a fundamental task in computer vision with numerous real-world applications, ranging from object tracking to video surveillance. Dynamic backgrounds poses a significant challenge here. Supervised deep learning-based techniques are currently considered state-of-the-art for this task. However, these methods require pixel-wise ground-truth labels, which can be time-consuming and expensive. In this work, we propose a weakly supervised framework that can perform background subtraction without requiring per-pixel ground-truth labels. Our framework is trained on a moving object-free sequence of images and comprises two networks. The first network is an autoencoder that generates background images and prepares dynamic background images for training the second network. The dynamic background images are obtained by thresholding the background-subtracted images. The second network is a U-Net that uses the same object-free video for training and the dynamic background images as pixel-wise ground-truth labels. During the test phase, the input images are processed by the autoencoder and U-Net, which generate background and dynamic background images, respectively. The dynamic background image helps remove dynamic motion from the background-subtracted image, enabling us to obtain a foreground image that is free of dynamic artifacts. To demonstrate the effectiveness of our method, we conducted experiments on selected categories of the CDnet 2014 dataset and the I2R dataset. Our method outperformed all top-ranked unsupervised methods. We also achieved better results than one of the two existing weakly supervised methods, and our performance was similar to the other. Our proposed method is online, real-time, efficient, and requires minimal frame-level annotation, making it suitable for a wide range of real-world applications.

翻译：背景减除是计算机视觉中的基础任务，在从目标跟踪到视频监控的众多实际应用中具有关键作用。动态背景对该任务构成显著挑战。当前，基于监督深度学习的算法被视为该领域的主流方法。然而，这些方法需要像素级的真值标签，标注过程耗时且成本高昂。本文提出一种弱监督框架，无需逐像素真值标签即可完成背景减除。该框架基于无运动物体图像序列进行训练，包含两个网络：第一个网络是自编码器，用于生成背景图像并制备用于训练第二个网络的动态背景图像（通过对减除背景后的图像设置阈值获得）；第二个网络是U-Net，利用同一无物体视频进行训练，并将动态背景图像作为像素级真值标签。在测试阶段，输入图像分别由自编码器和U-Net处理生成背景图像及动态背景图像。动态背景图像有助于消除减除背景后图像中的动态运动成分，从而获得无动态伪影的前景图像。我们在CDnet 2014数据集和I2R数据集的特定类别上进行了实验验证。结果表明，本方法性能优于所有顶尖无监督方法，同时优于两种现有弱监督方法之一，与另一方法性能相当。本方法具备在线、实时、高效的特点，且仅需极少量帧级别标注，适用于广泛的实际应用场景。