Motion Saliency Detection Based on Drosophila Vision-Inspired Model

Drosophila vision is extremely sensitive to moving targets and color opponency, which provides rich biological enlightenment for the study of computer vision. Drosophila vision has been extensively studied in various aspects, but our understanding of the underlying neural computation remains poorly understood. We propose a Drosophila vision-inspired model that constructs a complete visual motion perception system and a color processing system by integrating continuous computing layers to gain insight into the neural mechanisms of Drosophila vision and make better use of its strengths in saliency detection. Drosophila vision-inspired model can also be used for saliency detection in dynamic scenes, especially in some scenes where the color distinction is significant, it can accurately identify the motion of interest (MOI) while suppressing background interference and self-motion because our model depends on the motion perception and color opponency based on the Drosophila vision. Experiments on two large-scale video saliency detection datasets demonstrate the superiority of our model in saliency detection compared with the state-of-the art methods.