Sparse representation based background subtraction in videos

Background subtraction is an important preprocessing technique for a wide variety of problems in computer vision including automatic video surveillance, anomaly detection etc. Our focus is on background subtraction of videos taken from stationary cameras. We use sparse representation and compressive sensing to propose a novel algorithm that separate the background image and present the foreground objects in each frame. Our method is robust to dynamic background scenario where the background changes with time. We also point towards the fact that our algorithm is highly parallalizable and so can subtract background in real time. We demonstrate the superiority of our method against Mixture of Gaussian, KDE model and Monnnet´s method. Also our method is on par with AdaDGS in terms of visual result.