  Background modeling has emerged as a popular foreground detection technique
for various applications in video surveillance. Background modeling methods
have become increasing efficient in robustly modeling the background and hence
detecting moving objects in any visual scene. Although several background
subtraction and foreground detection have been proposed recently, no
traditional algorithm today still seem to be able to simultaneously address all
the key challenges of illumination variation, dynamic camera motion, cluttered
background and occlusion. This limitation can be attributed to the lack of
systematic investigation concerning the role and importance of features within
background modeling and foreground detection. With the availability of a rather
large set of invariant features, the challenge is in determining the best
combination of features that would improve accuracy and robustness in
detection. The purpose of this study is to initiate a rigorous and
comprehensive survey of features used within background modeling and foreground
detection. Further, this paper presents a systematic experimental and
statistical analysis of techniques that provide valuable insight on the trends
in background modeling and use it to draw meaningful recommendations for
practitioners. In this paper, a preliminary review of the key characteristics
of features based on the types and sizes is provided in addition to
investigating their intrinsic spectral, spatial and temporal properties.
Furthermore, improvements using statistical and fuzzy tools are examined and
techniques based on multiple features are benchmarked against reliability and
selection criterion. Finally, a description of the different resources
available such as datasets and codes is provided.
