Spatiotemporal GMM for Background Subtraction with Superpixel Hierarchy

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Abstract
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.
Funding Information
  • GRF Grant from the Research Grants Council of Hong Kong (CityU 122212)
  • City University of Hong Kong (9360153)
  • NSF CAREER (#1149783)
  • NSF IIS (#1152576)

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