论文标题
生成神经网络的动态背景减法
Dynamic Background Subtraction by Generative Neural Networks
论文作者
论文摘要
背景减法是计算机视觉中的重要任务，也是许多现实世界应用的重要步骤。背景减法方法的挑战之一是动态背景，在背景的某些地方构成了随机运动。在本文中，我们提出了一种新的背景减法方法，称为DBSGEN，该方法使用了两个生成神经网络，一种用于动态运动去除，另一种用于背景生成。最后，前景移动对象是通过基于动态熵图的像素距离阈值获得的。所提出的方法具有一个统一的框架，可以以端到端和无监督的方式进行优化。该方法的性能是通过动态背景序列评估的，并且优于大多数最新方法。我们的代码可在https://github.com/fatemebahri/dbsgen上公开获取。
Background subtraction is a significant task in computer vision and an essential step for many real world applications. One of the challenges for background subtraction methods is dynamic background, which constitute stochastic movements in some parts of the background. In this paper, we have proposed a new background subtraction method, called DBSGen, which uses two generative neural networks, one for dynamic motion removal and another for background generation. At the end, the foreground moving objects are obtained by a pixel-wise distance threshold based on a dynamic entropy map. The proposed method has a unified framework that can be optimized in an end-to-end and unsupervised fashion. The performance of the method is evaluated over dynamic background sequences and it outperforms most of state-of-the-art methods. Our code is publicly available at https://github.com/FatemeBahri/DBSGen.
