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 F-score and average accuracy percentage proved this, and, as such, it can be concluded that this new hybrid BGS technique improved upon existing techniques.
Highlights
Object extraction is a technique used in suppressing the background of a video scene to detect subjects that appear in the frame
The motivation of this work lies in the fact that most edge pixels are undetected after performing object extraction techniques based on frame difference (FD), approximate median (AM), running average (RA), and running Gaussian average (RGA). We have overcome this limitation by detecting all edge pixels; a perfect blob can be retrieved through morphological procedures. This is done by applying an second derivative in gradient direction (SDGD) filter on the results of background suppression and combining the foreground pixels generated by background subtraction (BGS) techniques with the detected edge as our extracted object
We provide a review of the literature on the four BGS techniques evaluated in this study, namely, frame differencing, approximate median, running average, and running Gaussian average
Summary
Object extraction is a technique used in suppressing the background of a video scene to detect subjects that appear in the frame. Prior research on background subtraction (BGS) used several parametric BGS techniques, such as running average [2,3,4], running Gaussian average [5,6,7], approximate median filter [7, 8], and Gaussian Mixture Model [9,10,11] These parametric techniques determine the foreground and update the subsequent background based on the distribution of intensity value [12]. We have overcome this limitation by detecting all edge pixels; a perfect blob can be retrieved through morphological procedures This is done by applying an SDGD filter on the results of background suppression and combining the foreground pixels generated by BGS techniques with the detected edge as our extracted object.
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