Using modified background subtraction for detecting vehicles in videos. (20th May 2022)

• Record Type: Journal Article
• Authors: Ata, Mohamed Maher; El-Darieby, Mohamed; El-nabi, Mustafa Abd; Napoleon, Sameh A.
• Is Part Of: International journal of advanced intelligence paradigms. Volume 22:Number 1/2(2022)
• Page Start: 21
• Page End: 36
• Publication Date: 2022-05-20
• Subjects: computer vision -- foreground object detection -- background subtraction -- video degradation; Artificial intelligence -- Periodicals; Machine theory -- Periodicals; Fuzzy logic -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-0386

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.