OR-PCA with Dynamic Feature Selection for Robust Background Subtraction
Résumé
Background modeling and foreground object detection is
the rst step in visual surveillance system. The task be-
comes more dicult when the background scene contains
signi cant variations, such as water surface, waving trees
and sudden illumination conditions, etc. Recently, subspace
learning model such as Robust Principal Component Analy-
sis (RPCA) provides a very nice framework for separating
the moving objects from the stationary scenes. However,
due to its batch optimization process, high dimensional data
should be processed. As a result, huge computational com-
plexity and memory problems occur in traditional RPCA
based approaches. In contrast, Online Robust PCA (OR-
PCA) has the ability to process such large dimensional data
via stochastic manners. OR-PCA processes one frame per
time instance and updates the subspace basis accordingly
when a new frame arrives. However, due to the lack of fea-
tures, the sparse component of OR-PCA is not always ro-
bust to handle various background modeling challenges. As
a consequence, the system shows a very weak performance,
which is not desirable for real applications. To handle these
challenges, this paper presents a multi-feature based OR-
PCA scheme. A multi-feature model is able to build a ro-
bust low-rank background model of the scene. In addition,
a very nice feature selection process is designed to dynami-
cally select a useful set of features frame by frame, according to the weighted sum of total features. Experimental results
on challenging datasets such as Wall
ower, I2R and BMC
2012 show that the proposed scheme outperforms the state
of the art approaches for the background subtraction task.