1 publications with the keyword "split gaussian"

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- art methods. 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 ). See: http://wordpress-jodoin.dmi.usherb.ca/method/121/

@inproceedings{Rui:FTSG-ChangeDetection-CVPRW-2014,
  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.