Fast Video Saliency Detection based on Feature Competition

In this paper, we propose a light video saliency prediction model, named SalFCM, which achieves fixation detection rate of 110fps. It is known that the human attention is captured by objects that have always been present or newly appeared. To model this dynamic change, we propose an Inter-frame Feature Competition Module (IFCM) to make an adaptive choice between correlated and differential features of consecutive frames. Besides, it is noted that saliency is better explained by low-level rather than high-level features in some visual scenes. Hence, we design a Hierarchical Feature Competition Module (HFCM) to balance the influence of low-level and high-level features. Our model achieves a good trade-off between precision and processing speed. The developed SalFCM is evaluated on three video saliency datasets: DHF1K, Hollywood-2 and UCF-sports. We conduct ablation studies to verify the effectiveness of the proposed model.