## 5 Experiment Results and Analysis  
In this section, we show our experimental results to assess the performance of our method in terms of its quality and speed. In addition, we provide comparisons between our strategy and the current well-known approaches. To eliminate wasting time in the image segmentation and shadow removal process, we have used techniques that are the quickest and are appropriate for low-powered equipment, such as local-global contrast enhancement, foreground segmentation with background subtraction, and a morphological restoration of the final shadow removed mask data. We implemented the proposed method in Visual Studio 2015 C++ and performed all experiments on a PC with a 3.60 GHz CPU and 4 GB of RAM. To evaluate the performance of the shadow removal method, the method has been tested in the widely used shadow detection CAVIAR and ATON datasets (hallway, lab, room and corridor) [27; 28]. Moreover, we employ indoor video sequences at a size of \(320\times 240\), obtained from Gachon University and the Tashkent University of Information Technologies (TUIT). To check the robustness and effectiveness of the shadow remover, we compare our method with popular commercial shadow detecting [23] software and state-of-the-art methods, such as the physical-based method (PBM) [29], gradient-based method (GBM) [24], and color-based method (CBM) [30]. Figures 3-6 show the results of visual comparison.  
Figure 4: Comparisons between reliable existing approaches and our method’s result: Input image (II); foreground mask for all shadow removal methods (FM); color-based method [30] (CBM); physical-based [29] method (PBM); texture-based [31] method (TBM); and our proposed method.  
Figure 3: Shadow removing process: (**a**) Background image, (**b**) current image, (**c**) foreground mask (with shadow), (**d**) shadow detection and removal process, (**e**) applied morphological restoration and final foreground mask without shadow pixels.  
Figure 3 shows the overall process of our proposed method in images (the video streams were taken at Gachon University in Korea). In these steps, we utilized a number of algorithms to apply to real practice. First, we apply a local-global contrast enhancement algorithm to the input background reference image and the current image in which the moving object exists. Then, we segment the foreground moving object out of the enhanced image by calculating pixel differences. The foreground subtraction algorithm provides us with a moving object mask, but it contains both the object and shadow pixels. A foreground subtraction approach cannot distinguish between a moving object and its shadow region. Such an error is caused by the fact that a moving object and its moving shadow share similar motional characteristics as demonstrated in Figure 3c. According to the above section, shadow removal is destructive procedure, in that it generates unwanted gaps and holes in the foreground mask. By utilizing the morphological operator, the consequences of the shadow removal process are eliminated (see Figure 3c). In the final step, we detect the original shape and edges of the moving object. The suggested method can be employed for smart city object tracking and security system applications.