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.
|Number of pages||8|
|Journal||IEEE transactions on pattern analysis and machine intelligence|
|Publication status||Published - 2018 Jun 1|
Bibliographical notePublisher Copyright:
© 1979-2012 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics