## 1 Introduction  
Moving shadow detection methods have been studied extensively by researchers [1]. They are profitable in various fields of computer vision application, such as in object tracking and foreground recognition. Currently, there are numerous frameworks and hardware systems for obtaining image sequences without any moving shadows or ghosts [2]. However, a variety of problems exist, such as background similarity and multiple shadows, which may make the shadow detection task difficult. We obtained video sequences to experiment with removing shadowed images. Moving foregrounds have become more accessible and can often show up in various positions in neighboring images, causing multiple objects to be detected as a single foreground blob [3].  
Shadow pixels are occasionally misclassified as parts of objects, causing mistakes in localization, segmentation, and the tracking of moving objects. Often, the shadows give significant data, for example, the relative position of an object from a source. Shadows can vary from the background surface in luminance and gray level, like the foreground objects in the scene. Furthermore, the utilization of picture shadow detection methods with minimally controlled equipment in real-time frameworks is a difficult issue [4], as the vast majority of the shadow removal techniques are computationally costly. Thus, a shadow removing method on a computer must be customized to fulfill the real-time requirements of video surveillance, with limited computational resources. This current study aims to address moving shadow detection problems, as mentioned above and in the following paragraph.  
The main contribution of this manuscript is that we present a simple, reliable, and automatic shadow removal method that is robust against background surface similarity and ghost problems. The method requires a small amount of computational time to detect the presence of moving objects for indoor video surveillance and is based on geometry features. Existing geometry-based methods do not adequately process shadow removal with objects having multiple shadows or where several objects are recognized as a single foreground mask. Our suggested algorithm is comprised of the following steps:  
* Enhancing input images based on combined contrast enhancement.
* Extracting moving objects using background subtraction and removing unwanted noises from the enhanced images.
* Detecting and removing shadow pixels that are correctly included in the candidate foreground mask.
* Applying a morphological reconstruction method to eliminate small gaps and holes from the moving object regions.
* Delivering the final result (without shadows) to the object tracking tasks.  
The rest of the paper is partitioned into section as follows: Section 2 shows a survey of some related shadow detection strategies. Section 3 provides a brief discussion of the difference between static and dynamic shadows. Section 4 gives a definite discourse on our proposed moving shadow removing method for dealing with moving object tracking issues. Section 5 demonstrates our test results and provides discussion based on the most widely-used databases. Section 6 provides conclusions based on the experimental results and future directions for study.