## 2 Literature Review  
Moving shadow detection has been investigated for years, and many researchers and scientists are working together in the shadow removing domain to reduce processing time and improve the quality of the segmentation result to deliver appropriate object tracking applications. However, shadow detection remains one of the most important and challenging issues in the areas of computer vision, object detection, and machine learning. Detecting shadow regions by the human eye may be a somewhat easy task, but it is a relatively challenging problem for a computer, as shadow pixels also simultaneously move as an object region. For these reasons, most contemporary studies concern detecting and removing shadows.  
[PERSON] et al. [5] introduced a shadow removing technique for moving objects to eliminate ghosting artifacts. In their approach, the ghosting area is rearranged to avoid removing the moving shadow pixels in the scene. However, this method is relatively difficult to use in urban surveillance and in multiple noise environments. [PERSON] et al. [6] utilized shadow features in the hue, saturation, value (HSV) color space to recognize shadow pixels where the object of interest is in motion. These properties demonstrate that cast shadows obscure the background in the luminance component, whereas the saturation and hue spaces change inside specific limits. The HSV color space was utilized because it provides a superior separation of chromaticity and grey level than other color spaces. In [7], the authors reviewed several shadow detection methods, each of which proved their efficiency in detecting and removing shadow pixels in indoor and outdoor environments. Several other research works were proposed for dynamic image sequences [8; 9; 10]. The scientists investigated the concept of including a multi-frame differencing system to enhance the division in situations where the shadows may not be effectively removed. [PERSON] et al. [11] suggested a new physics-based method that used luminance and intensity values to describe physical illumination changes.  
Recently published articles are different in terms of productivity and reliability [12; 13]. They try to overcome the modern problems in moving shadow detection, such as those in smart city and intelligence-building frameworks. Today, applying computer vision tasks to machine learning and neural networks is becoming a very important research area. [PERSON] et al. [14] employed a convolutional neural network (CNN) in their study, for shadow detection in images using a visible light camera sensor. The researchers presented a shadow detection and removal algorithm that used a \(21\times 21\) sliding window-based visual geometry group (VGG) \"Net-16\" CNN and showed a high accuracy, even in a high-definition surveillance condition. A new method for dynamic object and shadow detection based on motion prediction was proposed by [PERSON] et al. (2015), solving the shadow problem by using deep learning. In addition, applying a Markov random field enables a system to refine shadow detection results to improve its performance. In ([PERSON] et al., 2016), a novel approach is presented for versatile shadow removal by consolidating four distinct filters in a neuro-fuzzy structure. The neuro-fuzzy classifier has the capacity for real-time self-adaptation and training, and its execution has been quantitatively surveyed with both indoor and outdoor video streams.