## 1 Introduction  
Background subtraction methods are widely applied in the realm of computer vision applications, for example, object tracking [1], moving object detection [2], intelligent video surveillance [3], and human-computer interaction [4]. Detailed overviews of background subtraction methods are available in [5, 6]. In the past decade, several traditional background subtraction methods have been developed to segment foreground objects from the background in various video scenes. A parametric probabilistic background model, the Gaussian mixture model (GMM) [7], takes advantage of multiple normal distributions to fit the changes of pixels in order to deal with dynamic backgrounds, such as slow-moving objects, swaying trees, and water rippling. The Kernel density estimation (KDE) [8], a widely used nonparametric background method, estimates the distribution of pixels directly from the previously observed data without making any assumptions of the underlying distribution. [PERSON] et al. proposed using the visual background extractor (ViBe) method [9] for a sample-based background model. This method establishes the background model by aggregating previous input pixel information. The ViBe method adopts a stochastic renewal strategy for the phases of estimating and updating the background model and assumes that the information between adjacent pixels is transmitted [10]. Similarly, the pixel-based adaptive segmenter (PBAS) [11], which models the background by a history of recently observed pixel values, is also a nonparametric background modeling paradigm that introduces cybernetics to update threshold and background adaptively. The procedure for foreground detection by PBAS is similar to that by ViBe. Inspired by the low-cost and highly-efficient ViBe method, [PERSON] et al. presented the local binary similarity segmenter (LOBSTER) [12] method for background subtraction, which uses a spatiotemporal binary similarity descriptor instead of relying solely on pixel intensities as its core component. The SuBSENSE method was proposed by [PERSON] [13]. This method adopts the principle of sample consistency and feedback mechanism, and therefore the background model adapts to the diversity of a complex background. The fundamental idea of these traditional methods is as follows: First, establish a statistical background model with historical data. Subsequently, calculate the defined distance between a current pixel and the background. Once the pixel deviates from the background obviously, a foreground pixel is confirmed. Meanwhile, continue running the background maintenance to continuously update the background model. These methods have been widely used for detecting moving objects and they have achieved impressive results. The robust principal component analysis (RPCA) is a popular method used in background models [14]. In models using this method, the structure of the video is decomposed into low-rank and sparse matrices, which provide a suitable framework to separate moving objects from the background. [PERSON] [15] reviewed various models based on RPCA. Recently, inspired by the impressive achievement of deep learning, some researchers applied deep neural networks for background subtraction [16]. [PERSON] and [PERSON] [17] made use of convolutional neural networks (CNNs) to deal with the scene-specific problem of background subtraction, naming their model ConvNet, where the network architecture was inspired by the LeNet-5 network [18]. Their model consists of four phases: using a time median operation to extract gray background images from multiple initialization frames, generating the dataset for a specific scene, training the network, and background subtraction. In ConvNet, input image patches and corresponding background image patches are fed to the network to predict the probability of foreground pixels. Following this fundamental idea, [PERSON] et al. [19] trained a single CNN using various video scenes from the CD2014 dataset [20] to perform segmentation of the video sequences. [PERSON] et al.