OR-PCA with dynamic feature selection for robust background subtraction
Sajid Javed, Andrews Sobral, Thierry Bouwmans, Soon Ki Jung · 2015
Background modeling and foreground object detection is the first step in visual surveillance system. The task becomes more difficult when the background scene contains significant variations, such as water surface, waving trees and sudden illumination conditions, etc. Recently, subspace learning model such as Robust Principal Component Analysis (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 complexity 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 features, 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 dynamically select a useful set of features frame by frame, according to the weighted sum of total features. Experimental results on challenging datasets such as Wallflower, I2R and BMC 2012 show that the proposed scheme outperforms the state of the art approaches for the background subtraction task.