Spatiotemporal GMM for Background Subtraction with Superpixel Hierarchy
We propose a background subtraction algorithm using hierarchical superpixel segmentation, spanning trees and optical flow. First, we generate superpixel segmentation trees using a number of Gaussian Mixture Models (GMMs) by treating each GMM as one vertex to construct spanning trees. Next, we use the <inline-formula><tex-math notation="LaTeX">$M$ </tex-math><alternatives><inline-graphic xlink:href="yang-ieq1-2717828.gif"/></alternatives></inline-formula>-smoother to enhance the spatial consistency on the spanning trees and estimate optical flow to extend the <inline-formula> <tex-math notation="LaTeX">$M$</tex-math><alternatives><inline-graphic xlink:href="yang-ieq2-2717828.gif"/> </alternatives></inline-formula>-smoother to the temporal domain. Experimental results on synthetic and real-world benchmark datasets show that the proposed algorithm performs favorably for background subtraction in videos against the state-of-the-art methods in spite of frequent and sudden changes of pixel values.