Background subtraction via early recurrence in dynamic scenes

A biologically motivated model of background subtraction is proposed. The two-step computation borrows the idea from the low-level inhibitive processing of the two-pathway primate visual system. A spatiotemporal representation consistent with the dorsal pathway is computed and refined via center-surround inhibition. This representation catches perceptually salient foreground regions, and is further used to inhibit fine-scale visual features that are confined to the ventral pathway, leading to a high-spatially-accurate representation containing mostly foreground pixels. Output of our work is attached to a state-of-the-art visual saliency model. Results using real dynamic scenes are compared with ground truth, which confirmed that our early recurrent processing can effectively remove background.