Video object segmentation via adaptive threshold based on background model diversity
Mohamed Bachir Boubekeur, Senlin Luo, Hocine Labidi, Tarek Benlefki · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
The background subtraction could be presented as classification process when investigating the upcoming frames in a video stream, taking in consideration in some cases: a temporal information, in other cases the spatial consistency, and these past years both of the considerations above. The classification often relied in most of the cases on a fixed threshold value. In this paper, a framework for background subtraction and moving object detection based on adaptive threshold measure and short/long frame differencing procedure is proposed. The presented framework explored the case of adaptive threshold using mean squared differences for a sampled background model. In addition, an intuitive update policy which is neither conservative nor blind is presented. The algorithm succeeded on extracting the moving foreground and isolating an accurate background.