Vision‐based vehicle detection for road traffic congestion classification. (9th September 2020)

• Record Type: Journal Article
• Authors: Chetouane, Ameni; Mabrouk, Sabra; Jemili, Imen; Mosbah, Mohamed
• Is Part Of: Concurrency and computation. Volume 34:Number 7(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-09
• Subjects: traffic congestion detection -- traffic monitoring systems -- vehicle detection; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5983
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Due to the increasing number of vehicles in circulation in different urban cities, several automatic traffic monitoring systems have been developed. In particular, traffic monitoring systems using roadside cameras are becoming extensively deployed, as they offer imperative technological advantages compared with other traffic monitoring systems. Vehicle detection and traffic congestion classification are two main steps for video‐based traffic congestion detection systems; the associated methods have a deep impact on the performance of the whole system. In this paper, we investigate four selected vehicle detection methods namely Gaussian Mixture Model (GMM), GMM‐Kalman filter, Optical Flow, and ACF object detector in two contexts: urban and highway. Three traffic congestion classification methods are also studied. The comparative study of the different methods allows us to choose the most appropriate ones to be integrated in the framework proposed to solve the traffic issues in the bridge of Bizerte.