We added three saliencies maps up together to reflect dominant motion features into the attention model, i.e., the fused saliency map at each frame is adjusted by the top-down, static and motion saliency maps. The computational saliency map is superimposed by a set of saliency maps via different predefined approaches. In this paper, we proposed a generic Global Attention Model (GAM) system based on visual attention analysis. Motion features such as motion direction are assumed to be processed within the dorsal visual and the dorsal auditory pathways and there is no systematic approach to extract the motion cues well so far. Previous studies emphasized on static attention, however the motion features, which are playing key roles in human attention system intuitively, have not been well integrated into the previous models. Building attention models similar to human visual attention system should be very beneficial to computer vision and machine intelligence meanwhile, it has been a challenging task due to the complexity of human brain and limited understanding of the mechanisms underlying the human attention system. Abstract: Human vision system can optionally process the visual information and adjust the contradiction between the limited resources and the huge visual information.
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