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 M-smoother to enhance the spatial consistency on the spanning trees and estimate optical flow to extend the M-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. Next, we use the M-smoother to enhance the spatial consistency on the spanning trees, and estimate optical flow to extend the M-smoother to the temporal domain. Experimental results on synthetic and real-word benchmark datasets show that the proposed algorithm performs favorably for background subtraction in videos against state-of-the-art methods in spite of frequent and sudden changes of pixel values.