A Fast Moving Object Detection Technique using QR Decomposition based TV Regularized RPCA Framework

Moving Object Detection (MOD) is one of the prominent application in the field of computer vision. This paper contributes towards designing a fast and accurate MOD technique for real time applications by addressing the issues like dynamic background, illumination changes, occlusion, camera jitter, moving shadows, etc. The brilliance of the proposed method is confined in the effective utilization of QR decomposition and
$l_{2,1}$
-norm minimization in the well-known Robust Principal Component Analysis (RPCA) framework. The
$l_{2,1}$
norm is robust to outliers and exploits the low rankness of the background component. While the use of
$l_{1}$
-norm enhances the sparsity in the foreground part. Moreover, Total Variation(TV) regularization technique helps in retaining the details about the edges. Experimental result shows the supremacy of the proposed method w.r.t. the state of the art methods in terms of computational complexity and F-measure score.