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Real-time detection of moving objects in a video sequence by using data fusion algorithm

Tang, Chao and Hu, Huosheng and Zhang, Miaohui and Wang, Wen-Jian and Wang, Xiao-Feng and Cao, Feng and Li, Wen (2019) 'Real-time detection of moving objects in a video sequence by using data fusion algorithm.' Transactions of the Institute of Measurement and Control, 41 (3). pp. 793-804. ISSN 0142-3312

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The moving object detection and tracking technology has been widely deployed in visual surveillance for security, which is, however, an extremely challenge to achieve real-time performance owing to environmental noise, background complexity and illumination variation. This paper proposes a novel data fusion approach to attack this problem, which combines an entropy-based Canny (EC) operator with the local and global optical flow (LGOF) method, namely EC-LGOF. Its operation contains four steps. The EC operator firstly computes the contour of moving objects in a video sequence, and the LGOF method then establishes the motion vector field. Thirdly, the minimum error threshold selection (METS) method is employed to distinguish the moving object from the background. Finally, edge information fuses temporal information concerning the optic flow to label the moving objects. Experiments are conducted and the results are given to show the feasibility and effectiveness of the proposed method.

Item Type: Article
Uncontrolled Keywords: Moving object detection, edge detection, optic flow, Canny operation, security surveillance
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 08 Jul 2021 08:17
Last Modified: 13 Jan 2022 22:01

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