A Three-Way Optimization Technique for Noise Robust Moving Object Detection Using Tensor Low-Rank Approximation, l1/2, and TTV Regularizations

摘要

The rising demand for surveillance systems naturally necessitates more efficient and noise robust moving object detection (MOD) systems from the captured video streams. Inspired by the challenges in MOD which are yet to be addressed properly, this paper proposes a new MOD scheme using $l_{1/2}$ regularization in the tensor framework. It takes advantage of the special features of tensor singular value decomposition ( ${t}$ -SVD) along with regularizations using $l_{1/2}$ -norm with half thresholding operation and tensor total variation (TTV) to develop a noise robust MOD system with improved detection accuracy. While ${t}$ -SVD exploits the spatio-temporal correlation of the video background, $l_{1/2}$ regularization provides noise robustness besides removing the sparser but discontinuous dynamic elements in the spatio-temporal direction. Moreover, TTV enhances the spatio-temporal continuity and fills up the gaps due to the lingering objects and thereby extracting the foreground precisely. The proposed three-way optimization method is designed to address both static and dynamic background cases of MOD separately with the intention to reduce the misclassifications due to moving/cluttered background. The brilliance of this method is confirmed by the impressive visual quality of the background/foreground separation, noise robustness, reduced computational complexity, and rapid response. The quantitative evaluation discloses the predominance of the proposed method with respect to the state-of-the-art techniques.