Special issue on background modeling for foreground detection in real-world dynamic scenes

Excerpt

Although background modeling and foreground detection are not mandatory steps for computer vision applications, they may prove useful as they separate the primal objects usually called “foreground” from the remaining part of the scene called “background”, and permits different algorithmic treatment in the video processing field such as video surveillance, optical motion capture, multimedia applications, teleconferencing and human–computer interfaces. Conventional background modeling methods exploit the temporal variation of each pixel to model the background, and the foreground detection is made using change detection. The last decade witnessed very significant publications on background modeling but recently new applications in which background is not static, such as recordings taken from mobile devices or Internet videos, need new developments to detect robustly moving objects in challenging environments. Thus, effective methods for robustness to deal both with dynamic backgrounds, illumination changes in real scenes with fixed cameras or mobile devices are needed and so different strategies may be used such as automatic feature selection, model selection or hierarchical models. Another feature of background modeling methods is that the use of advanced models has to be computed in real-time and with low memory requirements. Algorithms may need to be redesigned to meet these requirements. Thus, the readers can find (1) new methods to model the background, (2) recent strategies to improve foreground detection to tackle challenges such as dynamic backgrounds and illumination changes, and (3) adaptive and incremental algorithms to achieve real-time applications. …