G. Seetharaman, and
Proc. of IEEE CVPR Workshop on Change Detection,
In this paper, we present a moving object detection system
named Flux Tensor with Split Gaussian models (FTSG)
that exploits the benefits of fusing a motion computation
method based on spatio-temporal tensor formulation, a
novel foreground and background modeling scheme, and a
multi-cue appearance comparison. This hybrid system can
handle challenges such as shadows, illumination changes,
dynamic background, stopped and removed objects. Extensive
testing performed on the CVPR 2014 Change Detection
benchmark dataset shows that FTSG outperforms state-ofthe-
R. Wang, F. Bunyak, G. Seetharaman, K. Palaniappan. Static and Moving Object Detection Using Flux Tensor with Split Gaussian Models, IEEE Change Detection Workshop, CVPR 2014 ( Best ranked method ).
author = "R. Wang and F. Bunyak and G. Seetharaman and K. Palaniappan",
title = "Static and moving object detection using flux tensor with split Gaussian models",
year = 2014,
booktitle = "Proc. of IEEE CVPR Workshop on Change Detection",
keywords = "ftsg, flux tensor, split gaussian, change detection",
doi = "10.1109/CVPRW.2014.68",
url = "http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2014/W12/papers/Wang_Static_and_Moving_2014_CVPR_paper.pdf"
R. Wang, F. Bunyak, G. Seetharaman, and K. Palaniappan. Static and moving object detection using flux tensor with split Gaussian models. Proc. of IEEE CVPR Workshop on Change Detection, 2014.