Abstract—In video surveillance, detection of moving objects
from an image sequence is very important for target tracking,
activity recognition, and behavior understanding. Background
subtraction is a very popular approach for foreground segmentation
in a still scene image. In order to compensate for illumination
changes, a background model updating process is generally
adopted, and leads to extra computation time. In this paper, we
propose a fast background subtraction scheme using independent
component analysis (ICA) and, particularly, aims at indoor
surveillance for possible applications in home-care and health-care
monitoring, where moving and motionless persons must be reliably
detected. The proposed method is as computationally fast as
the simple image difference method, and yet is highly tolerable to
changes in room lighting. The proposed background subtraction
scheme involves two stages, one for training and the other for detection.
In the training stage, an ICA model that directly measures
the statistical independency based on the estimations of joint and
marginal probability density functions from relative frequency
distributions is first proposed. The proposed ICA model can well
separate two highly-correlated images. In the detection stage, the
trained de-mixing vector is used to separate the foreground in a
scene image with respect to the reference background image. Two
sets of indoor examples that involve switching on/off room lights
and opening/closing a door are demonstrated in the experiments.
The performance of the proposed ICA model for background
subtraction is also compared with that of the well-known FastICA
algorithm.
