Illumination-Invariant Background Subtraction : Comparative Review, Models, and Prospects

Background subtraction is a key prerequisite for a wide range of image processing applications due to its pervasiveness in various contexts. In particular, video surveillance highly requires the reliable background subtraction for further operations, such as object tracking and recognition, and thus, enormous efforts for this task have been devoted in recent decades. However, the path of technological evolution for background subtraction has now faced with an important issue that has started to be resolved: sensitivity to dynamic changes of scene contexts (e.g., illumination variations and moving backgrounds). Such dynamic changes are hardly tolerated by most of traditional background models, since they yield the drastically different statistics of pixel values even onto the relevant position between consecutive frames. To resolve this problem, many researchers in this field have developed robust and efficient methods. The goal of this paper is to provide a comprehensive review with a special attention to schemes related to handling varying illuminations frequently occurring in the outdoor surveillance scenario. This paper covers a systematic taxonomy, methodologies, and performance evaluations on benchmark databases, and also provides constructive discussions for the smart video surveillance under unconstrained outdoor environments.