Spatiotemporal GMM for Background Subtraction with Superpixel Hierarchy
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
---|---|
Pages (from-to) | 1518-1525 |
Journal / Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Volume | 40 |
Issue number | 6 |
Online published | 20 Jun 2017 |
Publication status | Published - Jun 2018 |
Link(s)
DOI | DOI |
---|---|
Document Link | Links |
Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85021792303&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(3610c411-6993-47e6-a724-55b64a97a7bf).html |
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
Citation Format(s)
Spatiotemporal GMM for Background Subtraction with Superpixel Hierarchy. / Chen, Mingliang; Yang, Qingxiong; Li, Qing et al.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40, No. 6, 06.2018, p. 1518-1525.
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40, No. 6, 06.2018, p. 1518-1525.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review