The technology of frame interpolation can be applied in intelligent monitoring systems to improve the quality of surveillance video. In this paper, a region-guided frame interpolation algorithm is proposed by introducing two innovative improvements. On the one hand, a detection approach is presented based on visual correspondence for detecting the motion regions that correspond to attracted objects in video sequences, which can narrow the prediction range of interpolated frames. On the other hand, spatial and temporal mapping rules are proposed using coherency sensitive hashing, which can obtain more accurate predicted values of interpolated pixels. Experiments show that the proposed method can achieve encouraging performance in terms of visual quality and quantitative measures.