A novel saliency detection algorithm for videos is proposed in this paper. We adaptively determine the weights of color and motion features to extract combined global feature contrast by adopting the compactness prior of salient object. We localize a saliency searching area in a current frame using the saliency distribution computed at the previous frame. We estimate the saliency by computing a relative feature distance with respect to the salient object and local background, which is weighted by global feature contrast. Experimental results show that the proposed algorithm captures salient objects faithfully on various videos, and outperforms the state-of-the art video saliency detection methods qualitatively and quantitatively.