We propose a new Arithmetic Distribution Neural Network (ADNN) for learning the distributions of temporal pixels during background subtraction. In our ADNN, the arithmetic distribution operations are utilized to propose the arithmetic distribution layers, including the product distribution layer and the sum distribution layer. Furthermore, in order to improve the accuracy of the proposed approach, an improved Bayesian refinement model based on neighboring information, with a GPU implementation, is introduced. In the forward pass and backpropagation of the proposed arithmetic distribution layers, histograms are considered as probability density functions rather than matrices. Thus, the proposed approach is able to utilize the probability information of the histogram and achieve promising results with a very simple architecture compared to traditional convolutional neural networks. Evaluations using standard benchmarks demonstrate the superiority of the proposed approach compared to state-of-the-art traditional and deep learning methods. To the best of our knowledge, this is the first method to propose network layers based on arithmetic distribution operations for learning distributions during background subtraction.

该研究提出了一种基于算术分布神经网络（ADNN）的通用背景减除框架，利用算术分布层来学习时间像素的分布，并通过邻域信息和GPU实现的改进贝叶斯精化模型来提高准确性。研究结果表明，该方法在背景减除领域具有优越性。

基于算术分布神经网络的通用背景减除