An optimised background modelling for efficient foreground extraction. (2017)

• Record Type: Journal Article
• Authors: Sivagami, M.; Revathi, T.; Jeganathan, L.
• Is Part Of: International journal of high performance computing and networking. Volume 10:Number 1/2(2017)
• Page Start: 44
• Page End: 53
• Publication Date: 2017
• Subjects: particle swarm optimisation -- PSO -- foreground extraction -- optical flow -- GMM -- K-means clustering -- fuzzy C-means clustering -- background modelling -- videos -- surveillance systems -- security issues -- object detection -- video analysis; High performance computing -- Periodicals; Computer networks -- Periodicals; High performance computing; Periodicals; 004.05
• Languages: English
• ISSNs: 1740-0562

Abstract

Nowadays, analysing videos from a surveillance system in real-time is very important for resolving the security related social issues. Foreground extraction and object detection is a vital task in video analysis. In the proposed methods background, modelling is treated as an optimisation problem and solved using particle swarm optimisation. The background is modelled at regular intervals of time for adapting the changes in the environment. Then the background subtraction is applied to the current frame with the corresponding background modelled frame to extract the foreground. Added to it the optical flow applied image is compared with the foreground extracted image to avoid the false positives (FP) and false negatives (FN). This proposed foreground extraction technique for real-time videos gives results better than the previous algorithms with respect to the quality of extraction and space complexity.