Abstract
Shadows appear in many scenes. Human can easily distinguish shadows from objects, but it is one of the challenges for shadow detection intelligent automated systems. Accurate shadow detection can be difficult due to the illumination variations of the background and similarity between appearance of the objects and the background. Color and edge information are two popular features that have been used to distinguish cast shadows from objects. However, this become a problem when the difference of color information between object, shadow and background is poor, the edge of the shadow area is not clear and the shadow detection method is supposed to use only color or edge information method. In this article a shadow detection method using both color and edge information is presented. In order to improve the accuracy of shadow detection using color information, a new formula is used in the denominator of original c1 c2 c3. In addition using the hue difference of foreground and background is proposed. Furthermore, edge information is applied separately and the results are combined using a Boolean operator.
Highlights
Applications such as traffic monitoring and analysis, automatic surveillance systems and counting vehicles and missing objects
In order to improve the accuracy of shadow detection using color information, a new formula is used in the denominator of original c1 c2 c3
Shadow detection rate (η) which is related to the correct detected shadow pixels and shadow discrimination rate (ξ) which is related to the discrimination between shadow areas and objects and Fscore which is a balancing metric are three common metrics which are selected to show the accuracy of our method
Summary
Applications such as traffic monitoring and analysis, automatic surveillance systems and counting vehicles and missing objects. Image segmentation methods often cannot resolve two separate objects because of a shadow cast in between them and tend to detect the two objects as one object. Sometimes shadows are require an accurate method to detect foreground objects from a sequence. Background subtraction is a common method for detecting objects categorized as a separate object (Park and Lim, 2009). In real applications it is difficult to obtain a pure object the background and shadow. As an example of this kind of methods, foreground segmentation is done by noise level adapted method in Wei-Gang and Bin (2010). Edges of shadow are removed by geometric scanning and holes of object are filled using foreground’s skeleton
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