Enhancement of Background Subtraction Techniques Using a Second Derivative in Gradient Direction Filter

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. 1. Introduction Object extraction is a technique used in suppressing the background of a video scene to detect subjects that appear in the frame. The technique involves comparing or subtracting the current frames from the background frame and treating the remaining pixels as foreground [1]. Prior research on background subtraction (BGS) used several parametric BGS techniques, such as running average [2–4], running Gaussian average [5–7], approximate median filter [7, 8], and Gaussian Mixture Model [9–11]. These parametric techniques determine the foreground and update the subsequent background based on the distribution of intensity value [12]. Aside from these techniques, other studies have introduced nonparametric models that detect foreground and background based on the intensity of statistical properties [13]. Other non-parametric models include a kernel density estimator [14] and mean shift estimation [15]. This work focuses on basic BGS techniques, such as frame differencing, approximate median, running average, and running Gaussian average. The motivation of this work lies in the fact that most edge pixels are undetected after performing object extraction techniques based on FD, AM, RA, and RGA. In this study, however, we have overcome this limitation by detecting all edge pixels; hence 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