# Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments
## 1 Introduction
In response to this idea in [30], using the least essential feature vector as an alternative solution and improving the background model representation is recommended. Simplified Self-Organized Background Subtraction (SOBS). In this model, each color pixel is mapped to a neural map of \(n\) segments. This map is the background model, and each current pixel is evaluated to find the best match. That is, the Euclidean distance is used to find the minimum distance between the intensity of the current color and the neural map [31; 32; 33]. The advantage of this model is to adapt to gradual lighting changes or dynamic backgrounds. Even so, the shadow cast by the object will be detected and included in the reconstructed background model. Dynamic Mode Decomposition (DMD). Despite being a method used to analyze the behavior of fluids [34; 35; 36; 37; 38], ref. [39] used it for image analysis, considering a video sequence as a dynamic fluid. A matrix decomposition is carried out from the image sequences, which will be propagated to a matrix, from which the singular value decomposition is obtained. The eigenvectors of this decomposition are dynamic patterns, and the values represent the temporal dynamics of these patterns. This technique allows fast and scalable decomposition of video sequences. Sliding Window-Based Change Detection (SWCD). It was introduced by [40]. Among them, the dynamic changes of pixel intensity are detected and adjusted to the background image. In addition, this approach features a sliding window and dynamic control to update the background image and perform background subtraction. According to the authors, this method overcomes intermittent changes in lighting, camera vibration, and moving objects. However, removing misclassified pixels depends on the window size [41]. This method is applied in various studies, including the analysis of eucalyptus plantation [42], change detection on the Earth's surface [43], and the dynamic inference of airport flight ground service networks [44]. A universal background subtraction algorithm (ViBe). This method was proposed by [45] and has been widely used in scenes with dynamic background [46], camera movement [47], or foggy scene [48], because of its easy implementation and high efficiency. The proposal consists of storing a set of past values for each neighborhood pixel. Then, the set is compared to determine if each pixel belongs to the background model or if the model must be adapted to these changes. Finally, the neighboring pixels are evaluated when the pixel is classified as the background. However, ref. [49] identifies problems such as the ghost effect, sensitivity to shadows, or sensitivity to the target's movement speed. Gaussian Mixture Model (GMM). This method was introduced by [6]. It has been widely accepted in the literature [50] and is one of the primary references because it is a powerful tool for grouping. Generally, this method characterizes each newly observed pixel value as a Gaussian mixture representing the background pattern. If the observed pixels do not match any Gaussian distribution, the distribution with the least probability is replaced by the new parameter. However, there are difficulties with shadows, irregular background motion, objects that stop suddenly, or objects that maintain a similar intensity to the background. Nevertheless, the model has been proven to be stable outdoors and reliable for light or long-term changes in the scene [51]. Euclidean distance (DEU). It is a simple background model where moving objects can be detected with the Euclidean distance measure. The lighting changes are updated iteratively with the previous image as the background model. However, it is not robust in the face of changes in light, stationary objects, shadows, and ghost effects [52]. Deep Learning Methods. In recent years, the adoption of deep learning techniques for computer vision applications has surged due to their successful implementation.