To handle the problems encountered in traditional moving object detecting algorithms under complex scenarios, by comprehensively using the Gaussian mixture model(GMM), mean filter and codebook(CB), a fusion-based detection approach is proposed based on cautious ordered weighted averaging with evidential reasoning(COWA-ER). In the proposed approach, a multi-criteria decision-making framework is established, where the three detection algorithms are used as multiple criteria. The double threshold method is used to model the uncertainty in the detection. The decision-level fusion for detection is finally accomplished by using COWA-ER, where complementary advantages of the three algorithms can be fully used. Experimental results show that the proposed approach can achieve better performance for the detection, and effectively deal with the influence of detection performance caused by shadow and light change.