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 c 1 c 2 c 3 . 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.

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Problem statement: In many visions-based surveillance systems, the first step is accomplished by detecting moving objects resulted from subtraction of the current captured frame from the extracted background. So, the results of these systems mainly depend on the accuracy of the background image. Approach: In this study, a proposed background extraction system is presented to model the background using a simple method, to initialize the model, to extract the moving objects and to construct the final background. Our model saves the history of each pixel separately. It uses the saved information to extract the background using a probability-based method. It updates the history of the pixel consequently and according to the value of that pixel in the current captured image. Results: Results of the experiments certify that not only the quality of the final extracted background is the best between four recently re-implemented methods, but also the time consumption of the extraction is acceptable. Conclusion: Since History-based methods use temporal information extracted from the several previous frames, they are less sensitive to noise and sudden changes for extracting the background image.