An adaptive spatiotemporal saliency algorithm for video attention detection using motion vector decision is proposed, motivated by the importance of motion information in video sequences for human visual system. This novel system can detect the saliency regions quickly by using only part of the classic saliency features in each iteration. Motion vectors calculated by block matching and optical flow are used to determine the decision condition. When significant motion contrast occurs (decision condition is satisfied), the saliency area is detected by motion and intensity features. Otherwise, when motion contrast is low, color and orientation features are added to form a more detailed saliency map. Experimental results show that the proposed algorithm can detect salient objects and actions in video sequences robustly and efficiently.
['Yaping Zhu', 'Natan Jacobson', 'Hong Pan', 'Truong Q. Nguyen']
Motion-decision based spatiotemporal saliency for video sequences