A novel video saliency detection method is proposed based on the spatiotemporal features of superpixels, which is used to the superpixel segmentation of images and extract the features of color gradient and motion gradient for the construction of a spatial-temporal gradient map of superpixels. The average weighted geodesic distance is used to measure the spatiotemporal saliency degree of each superpixel relative to its neighbor on the spatiotemporal gradient map, and thus the spatiotemporal saliency map is formed. In order to obtain the motion coherency map, the motion entropy in the multiple continuous frames is computed to represent the motion coherence of motion object over time. The fusion of spatiotemporal saliency maps and motion coherency maps is applied to locate in the salient motion using adaptive segmentation. In addition, the performance of the proposed method is compared with those of the other algorithms in experiments from two perspectives of visual analysis and qualitative evaluation. The results show that the proposed method is robust and suitable for the detection of moving targets in videos within complex background texture and changeable environment. Moreover, the detection precision is up to 92%.