Foreground detection using spatiotemporal projection kernels

Yair Moshe, Hagit Hel-Or, Yacov Hel-Or

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In this paper, we propose a novel video foreground detection method that exploits the statistics of 3D spacetime patches. 3D space-time patches are characterized by means of the subspace they span. As the complexity of real-time systems prohibits performing this modeling directly on the raw pixel data, we propose a novel framework in which spatiotemporal data is sequentially reduced in two stages. The first stage reduces the data using a cascade of linear projections of 3D space-time patches onto a small set of 3D Walsh-Hadamard (WH) basis functions known for its energy compaction of natural images and videos. This stage is efficiently implemented using the Gray-Code filtering scheme [2] requiring only 2 operations per projection. In the second stage, the data is further reduced by applying PCA directly to the WH coefficients exploiting the local statistics in an adaptive manner. Unlike common techniques, this spatiotemporal adaptive projection exploits window appearance as well as its dynamic characteristics. Tests show that the proposed method outperforms recent foreground detection methods and is suitable for real-time implementation on streaming video.

Original languageEnglish
Title of host publication2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
Number of pages8
StatePublished - 2012
Event2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 - Providence, RI, United States
Duration: 16 Jun 201221 Jun 2012

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919


Conference2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
Country/TerritoryUnited States
CityProvidence, RI

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Foreground detection using spatiotemporal projection kernels'. Together they form a unique fingerprint.

Cite this