Enhancement of background subtraction techniques using a second derivative in gradient direction filter

Authors:
Farah Yasmin Abdul Rahman;Aini Hussain;Wan Mimi Diyana Wan Zaki;Halimah Badioze Zaman;Nooritawati Md Tahir
Affiliations:
Department of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia, Selangor, Malaysia;Department of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia, Selangor, Malaysia;Department of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia, Selangor, Malaysia;Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Selangor, Malaysia;Faculty of Electrical Engineering, Universiti Teknologi MARA, Selangor, Malaysia
Venue:
Journal of Electrical and Computer Engineering
Year:
2013

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Abstract

A new approach was proposed to improve traditional background subtraction (BGS) techniques by integrating a gradient-based edge detector called a second derivative in gradient direction (SDGD) filter with the BGS output. The four fundamental BGS techniques, namely, frame difference (FD), approximate median (AM), running average (RA), and running Gaussian average (RGA), showed imperfect foreground pixels generated specifically at the boundary. The pixel intensity was lesser than the preset threshold value, and the blob size was smaller. The SDGD filter was introduced to enhance edge detection upon the completion of each basic BGS technique as well as to complement the missing pixels. The results proved that fusing the SDGD filter with each elementary BGS increased segmentation performance and suited postrecording video applications. Evidently, the analysis using Fscore and average accuracy percentage proved this, and, as such, it can be concluded that this new hybrid BGS technique improved upon existing techniques.