A graph-based video saliency detection algorithm is proposed in this work. We model eye movements on an image plane as random walks on a graph. To detect the saliency of the first frame in a video sequence, we construct a fully connected graph, in which each node represents an image block. We assign an edge weight to be proportional to the dissimilarity between the incident nodes and inversely proportional to their geometrical distance. We extract the saliency level of each node from the stationary distribution of the random walker on the graph. Next, to detect the saliency of each subsequent frame, we add the criterion that an edge, connecting a slow motion node to a fast motion node, should have a large weight. We then compute the stationary distribution of the random walk with restart (RWR) simulation, in which the saliency of the previous frame is used as the restarting distribution. Experimental results show that the proposed algorithm provides more reliable and accurate saliency detection performance than conventional algorithms.