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Background modeling has emerged as a popular foreground detection techniquefor various applications in video surveillance. Background modeling methodshave become increasing efficient in robustly modeling the background and hencedetecting moving objects in any visual scene. Although several backgroundsubtraction and foreground detection have been proposed recently, notraditional algorithm today still seem to be able to simultaneously address allthe key challenges of illumination variation, dynamic camera motion, clutteredbackground and occlusion. This limitation can be attributed to the lack ofsystematic investigation concerning the role and importance of features withinbackground modeling and foreground detection. With the availability of a ratherlarge set of invariant features, the challenge is in determining the bestcombination of features that would improve accuracy and robustness indetection. The purpose of this study is to initiate a rigorous andcomprehensive survey of features used within background modeling and foregrounddetection. Further, this paper presents a systematic experimental andstatistical analysis of techniques that provide valuable insight on the trendsin background modeling and use it to draw meaningful recommendations forpractitioners. In this paper, a preliminary review of the key characteristicsof features based on the types and sizes is provided in addition toinvestigating their intrinsic spectral, spatial and temporal properties.Furthermore, improvements using statistical and fuzzy tools are examined andtechniques based on multiple features are benchmarked against reliability andselection criterion. Finally, a description of the different resourcesavailable such as datasets and codes is provided.

On the Role and the Importance of Features for Background Modeling and Foreground Detection