Abstract
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.
Highlights
Big data research is attracting a great amount of attention due to the seemingly infinite generation of huge data worldwide
Due to the tremendous volume of data generated by urban surveillance systems, big data-oriented lowcomplexity automatic background subtraction techniques are in great demand
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
Summary
Big data research is attracting a great amount of attention due to the seemingly infinite generation of huge data worldwide. Big dataoriented techniques have emerged as an important research topic for smart cities and urban surveillance systems [1][2]. Urban surveillance systems are important applications for smart cities [3][4]. These systems use automated object detection methods whereby cameras are installed to automatically detect vehicles and other objects. Because of the tremendous volume of data generated by urban surveillance systems, to obtain useful information from the huge numbers of images and videos, low-complexity techniques that can automatically identify objects from various sources are in high demand. Automated or so-called automatic object detection algorithms have become
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