Big Data Oriented Novel Background Subtraction Algorithm for Urban Surveillance Systems - Ling Hu; Qiang Ni; Feng Yuan (Big Data Mining and Analytics)
Due to the tremendous volume of data generated by urban surveillance systems, big data oriented low-complexity automatic background subtraction techniques are in great demand. In this paper, we propose a novel automatic background subtraction algorithm for urban surveillance systems in which the computer can automatically renew an image as the new background image when no object is detected. This method is both simple and robust with respect to changes in light conditions.