Automated class identification of modes of travel in shared spaces: a case study from India

Zaki, Mohamed H.; Sayed, Tarek

doi:10.1049/iet-its.2017.0099

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2020

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Cited by 2 publications

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“…The role of powered two‐wheelers in developing countries is given in detail [27]. Unique traffic problems that are directly caused by the high number of scooter users are discussed in [28, 29].…”

Section: Related Workmentioning

confidence: 99%

Expanding vision‐based ADAS for non‐structured environments

Antony

Manikandan

2020

IET Intelligent Transport Systems

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Section: Related Workmentioning

confidence: 99%

Expanding vision‐based ADAS for non‐structured environments

Antony

Manikandan

2020

IET Intelligent Transport Systems

View full text Add to dashboard Cite

Comparative Analysis of Background Subtraction and CNN Algorithms for Mid-Block Traffic Data Collection and Classification

Illahi¹,

Mir²

2020

Int J Math, Eng, Manag Sci

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Classification of vehicles in the traffic stream is a pre-requisite for planning and designing the facilities for road-users. Considering the importance and gaining popularity of automated systems in this field, the aim of this article is to compare two algorithms- one using the Background Subtraction (BS) technique and the other using Convolutional Neural Network (CNN) with a primary focus on an increased number of vehicle classifications. To check the reliability of these algorithms, the outputs produced were validated against the data obtained from Kachkoot Toll Plaza, India. The results were analyzed using drop-line diagrams and confusion matrices. The overall efficiency of the CNN-based algorithm (0.98) was found to be better than the BS-based algorithm (0.95). The comparison presented in this paper will be useful for transportation professionals and agencies.

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Automated class identification of modes of travel in shared spaces: a case study from India

Cited by 2 publications

References 52 publications

Expanding vision‐based ADAS for non‐structured environments

Expanding vision‐based ADAS for non‐structured environments

Comparative Analysis of Background Subtraction and CNN Algorithms for Mid-Block Traffic Data Collection and Classification

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