Developing and Validating a Simulation Model for Counting and Classification of Vehicles

Jehad, Ali Emad; Rahmat, Riza Atiq O.K.

doi:10.17576/jkukm-2016-28-04

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…Shadow removal is an image processing step aimed at enhancing the quality of a video or image for computer systems. Jehad et al [113] developed a fast vehicle detection and counting method using a video camera. A system is presented for extracting traffic data using video image processing using background subtraction, shadow removal, and pixel analysis.…”

Section: Vision-based Methodsmentioning

confidence: 99%

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Shokravi

Bakhary

et al. 2020

Sensors

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Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques.

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Section: Vision-based Methodsmentioning

confidence: 99%

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Shokravi

Bakhary

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

Developing and Validating a Simulation Model for Counting and Classification of Vehicles

Abstract: An algorithm system approach has been presented for extracting traffic data using video image processing. While

Cited by 1 publication

References 9 publications

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

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