## 5 Experiment Results and Analysis
### Qualitative Results  
To prove the accuracy of our framework in eliminating ghost artifacts (shadows existing on both the wall and floor) and multiple shadows, more experiments are necessary. Figure 4 illustrates  
Figure 5: Visual comparison between previous approaches and proposed method: input image (II); foreground mask for all shadow removal methods (FM); gradient-based method (GBM) [24]; physical-based method (PBM) [29]; color-based method (CBM) [30]; and our proposed method.  
Figure 6: Quantitative comparisons of shadow removal approaches using the CAVIAR 10k dataset.  
comparisons between the proposed method and other state-of-the-art shadow removal algorithms, using similar foreground masks from the background subtraction section. The shadow is caused by the indoor light source and the results show that most of the existing shadow removal methods fail to generate a foreground mask without shadow pixels or significantly distort the object's shape. Our experimental results show that, in many cases, such as indoor scenes, a physical-based method faces problems when objects have spread-out shadow regions. In contrast, the scheme easily determines and removes monodirectional shadow pixels in moving objects. Moreover, ghost effects also make removal a difficult task. In addition, in the area where shadow pixels removed, salt and pepper noise occurs in PBMs. We addressed that problem by utilizing by the morphological approach in the last step of our method.  
Texture-based methods are potentially powerful methods for detecting shadows, as textures are highly distinctive, do not depend on colors, and are robust to illumination changes [30; 31]. A color-based approach fails when objects are darker and have colors similar to that of the background surface. A texture-based shadow detector better emphasizes the moving object, because this method also uses global contrast enhancement in the preprocessing step, as described in [23]. Furthermore, it effectively solves the problem occurring when multiple objects have multidirectional shadow regions. However, it has not yet been tested regarding ghost artifacts and background similarity issues. The results of our method appear to be attractive and accurate, as compared to the results of others. The moving shadow problems are addressed, and a seamless result is achieved using our method.  
In the above experiments, we have mainly tested the performance of shadow remover frameworks on a single object. In Figure 5, we implement all of the selected methods to examine how they work on multiple objects. It is known that removing shadow pixels from multiple objects is a more difficult task than removing them from one object. For example, ghost effects cause a doubling of walking people on the foreground mask and it can appear unclear, or several objects can appear as a single moving blob on the foreground mask (kernel). From the results, we could see that our method works effectively, even when there are many objects in an image sequence, as shown in Figure 5. Our improved shadow removal method delineates foreground objects more accurately in indoor (e.g., corridor) video surveillance. In addition, in many cases, both our method and a gradient-based method (GBM) perform well on handling multiple dynamic object problems. In contrast, physical-based method (PBM) and color-based method (CBM) strategies fail most of the time and misclassify the moving shadow region as an object region, or as a part of them.