Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 40, Issue 6, Pages 1518-1525Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2017.2717828
Keywords
Background modeling; superpixel hierarchy; minimum spanning tree; tracking; optical flow
Funding
- GRF grant from the Research Grants Council of Hong Kong (RGC) [CityU 122212]
- City University of Hong Kong [9360153]
- NSF CAREER Grant [1149783]
- NSF IIS Grant [1152576]
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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.
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