Authors: M. Sivagami; T. Revathi; L. Jeganathan
Addresses: APSG, SCSE, VIT University, Chennai, India ' JRF, DST, De1hi, India ' SCSE, VIT University, Chennai, India
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.
Keywords: 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.
International Journal of High Performance Computing and Networking, 2017 Vol.10 No.1/2, pp.44 - 53
Available online: 13 Mar 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article