Spatiotemporal GMM for Background Subtraction with Superpixel Hierarchy

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

89 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)1518-1525
Journal / PublicationIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume40
Issue number6
Online published20 Jun 2017
Publication statusPublished - Jun 2018

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. 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.

Research Area(s)

  • Background Modeling, Superpixel Hierarchy, Minimum Spanning Tree, Tracking, Optical Flow