Oncel Tuzel (1,3) Fatih Porikli (3) and Peter Meer (1,2)
(1)Department of Computer Science
(2)Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
(3) Mitsubishi Electric Research Laboratories
Cambridge, MA 02139
Learning background statistics is an essential task for several
visual surveillance applications such as incident detection
and traffic management. In this paper, we propose
a new method for modeling background statistics of a dynamic
scene. Each pixel is represented with layers of Gaussian
distributions. Using recursive Bayesian learning, we
estimate the probability distribution of mean and covariance
of each Gaussian. The proposed algorithm preserves
the multimodality of the background and estimates the number
of necessary layers for representing each pixel. We
compare our results with the Gaussian mixture background
model. Experiments conducted on synthetic and video data
demonstrate the superior performance of the proposed approach.
IEEE International Workshop on Machine Vision for Intelligent Vehicles
, San Diego, CA, June 2005 (in conjunction with CVPR'05)
Return to Research: Robust Analysis of Visual Data
