Effective background modelling and subtraction approach for moving object detection. Issue 1 (1st February 2015)

• Record Type: Journal Article
• Authors: Liu, Wei; Yu, Hongfei; Yuan, Huai; Zhao, Hong; Xu, Xiaowei
• Is Part Of: IET computer vision. Volume 9:Issue 1(2015)
• Page Start: 13
• Page End: 24
• Publication Date: 2015-02-01
• Subjects: object detection -- image motion analysis -- learning (artificial intelligence); background subtraction approach -- real-time moving object detection -- pixel-wise background modelling method -- coarse detection -- borrowâ€"lend strategy -- flexible learning rate -- illumination changes -- block-wise foreground validation approach -- refined detection; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2013.0242
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : 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.