Using modified background subtraction for detecting vehicles in videos Online publication date: Mon, 23-May-2022
by Mohamed Maher Ata; Mohamed El-Darieby; Mustafa Abd El-nabi; Sameh A. Napoleon
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 22, No. 1/2, 2022
Abstract: In this paper, a comparison study has been introduced between the traditional foreground detector (background subtraction technique) and a modified background subtraction-based detector (empty frame subtraction technique). A case study for such analysis has been introduced for estimating the average vehicular speed and the level of crowdedness in three test traffic videos with five different indices; frame rate, resolution, number of frames, duration, and extension). The proposed modification in the background subtraction detector strategy aims to reduce vehicle detection processing time which increase vehicle tracking efficacy. In addition, video degradations (salt and pepper noise, Gaussian noise, and speckle noise) have been applied in both traditional and modified background subtraction. Results have reflected an obvious decrease in the processing time by almost 40% than the traditional background detector.
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