ADM-HIPaR: An efficient background subtraction approach
Thien Huynh‐The, Sungyoung Lee, Cam-Hao Hua · 2017
This paper presents a novel background subtraction method that is flexible for various background scenarios. The method includes automated-directional masking (ADM) algorithm for adaptive background modeling and historical intensity pattern reference (HIPaR) algorithm for foreground segmentation. By selecting an appropriate mask in a set based on directional feature, ADM updates background smoothly and precisely following a boundary-based strategy with an intensity correction rule. In order to segment foreground, HIPaR refers intensity patterns of previous backgrounds and input frames and then compares their mean difference with a checking threshold to make foreground decision. Experimental results prove that our proposed ADM-HIPaR outperforms other state-of-the-art methods in terms of foreground detection accuracy.