Change Detection Based on Binary Mask Enhancement

Abstract

Detecting changes in a video sequence or still images is a crucial task in the image processing domain, which aims to distinguish moving objects from static ones. This functionality has a lot of applicability in the image processing domain like mostly in the surveillance cameras in organizations both the government and private. This change detection has captured a lot of attention in recent years due to its use and achievement in the domain. In this work, fast spatiotemporal tree filter (FSTF) method is used to enhance the detection results. It combines the features of the local and global filter that makes it efficient and better in comparison to other filters. Experiments demonstrate that the proposed FSTF filter improves the detection results of various foreground detection approaches, ranging from background modeling to machine learning model.