Background Subtraction for Automated Multisensor Surveillance: A Comprehensive Review

Background subtraction is a widely used operation in the video surveillance, aimed at separating the expected scene (the background) from the unexpected entities (the foreground). There are several problems related to this task, mainly due to the blurred boundaries between background and foreground definitions. Therefore, background subtraction is an open issue worth to be addressed under different points of view. In this paper, we propose a comprehensive review of the background subtraction methods, that considers also channels other than the sole visible optical one (such as the audio and the infrared channels). In addition to the definition of novel kinds of background, the perspectives that these approaches open up are very appealing: in particular, the multisensor direction seems to be well-suited to solve or simplify several hoary background subtraction problems. All the reviewed methods are organized in a novel taxonomy that encapsulates all the brand-new approaches in a seamless way.