Shadow detection is a key problem in object detection

object tracking and visual surveillance. In this paper

a new shadow detection algorithm is proposed based on fuzzy Markov random fields. The shadow detection problem is regarded as a problem of searching the optimal labeling of the total foreground pixels. The background abstract algorithm is used to find out the shadow and foreground pixels in the image. The fuzzy Markov random fields are created after the calculation of the shadow probabilities

the foreground probabilities and the membership functions. Bayesian principle

maximum a posteriori(MAP) estimation

iterated conditional mode(ICM) algorithm are used to search the optimal fuzzy Markov random field. The result of the shadow detection is obtained by defuzzifying the fuzzy Markov random field according to the maximum membership principle. Experimental results demonstrate the performance of the proposed algorithm.