Fusion-based Gaussian mixture model for background subtraction from videos
by T. Subetha; S. Chitrakala; M. Uday Theja
International Journal of Computer Applications in Technology (IJCAT), Vol. 66, No. 1, 2021

Abstract: Human Activity Recognition (HAR) aims to realise and interpret human activities from videos, and it comprises background subtraction, feature extraction and classification stages. Among those stages, the background subtraction stage is mandatory to achieve a better recognition rate while analysing the videos. The proposed Fusion-based Gaussian Mixture Model (FGMM) background subtraction algorithm extracts the foreground from videos invariant to illumination, shadows and the dynamic background. The proposed FGMM algorithm consists of three stages: background detection, colour similarity and colour distortion calculation. Here, the Jefries-Matusita distance measure is utilised to check whether the current pixel matches the Gaussian distribution, and by using this value, the background model is updated. Weighted Euclidean based colour similarity measure is used to eliminate shadows, and colour distortion measure is adopted to handle illumination variations. The extracted foreground is binarised to easily extract the foreground's interest points, which has white pixels stored into the frame. This algorithm has experimented over test sets gathered from publicly available benchmark data sets such as the K-th data set, Weizmann data set, PETS data set and change detection data set. Experimental results have proved that the proposed FGMM exhibits better accuracy in foreground detection, with better accuracy than the prevailing approaches.

Online publication date: Sat, 11-Dec-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com