Abstract: Shadows cause problems in many computer vision tasks, including image segmentation, object recognition, and edge detection. Shadow detection can be used to avoid the above-mentioned problems and can aid in shadow removal. Therefore, shadow detection is a popular topic in both image processing and in computer vision. Many shadow detection algorithms have been proposed in recent years. Currently, several review articles for moving shadow detection algorithms have been published; however, such a paper has not yet appeared for static shadow detection algorithms. In this study, we divide recent static shadow detection approaches into three categories: model-based methods, intrinsic image-based methods, and statistical learning-based methods. We survey and summarize the current status of these areas of research. We also discuss the open problems and future development.