Open AccessEffective background modelling and subtraction approach for moving object detection
Author(s) -
Liu Wei,
Yu Hongfei,
Yuan Huai,
Zhao Hong,
Xu Xiaowei
Publication year - 2015
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2013.0242
Subject(s) - background subtraction , computer science , robustness (evolution) , artificial intelligence , object detection , pixel , computer vision , foreground detection , block (permutation group theory) , pattern recognition (psychology) , subtraction , mathematics , arithmetic , biochemistry , chemistry , geometry , gene
This study presents a hierarchical background modelling and subtraction approach for real‐time detection of moving objects. At the first level, a novel pixel‐wise background modelling method is proposed for coarse detection. The method can dynamically assign the optimal number of components for each pixel with the borrow–lend strategy. And a flexible learning rate which is variable and different for each component is presented to adapt to scene changes. Additionally, a new mechanism using a framework of finite state machine is introduced to maintain and update the background models. At the second level, in order to deal with sudden illumination changes, a block‐wise foreground validation approach is adopted for refined detection. The authors compare the proposed approach with state‐of‐the‐art methods and experimental results under various scenes demonstrate the robustness and effectiveness of the proposed approach.