Vehicle Classification Based on Multiple Fuzzy C-Means Clustering Using Dimensions and Speed Features

Javadi, Saleh; Rameez, Muhammad; Dahl, Mattias; Pettersson, Mats I.

doi:10.1016/j.procs.2018.08.085

Cited by 21 publications

(10 citation statements)

References 9 publications

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“…Numerous machine learning techniques including supervised (classification) and unsupervised (clustering) methods have been applied to the classification of vehicles (2,(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31). However, only a limited number of studies have used geometric measurements such as width, length, height, volume, angle size, and area for classification purposes (24)(25)(26)(27)(28)(29)(30)(31). Among those using clustering techniques, Javadi et al (24) classify vehicles into ''private car,'' ''light trailer,'' ''lorry or bus,'' and ''heavy trailer,'' using dimension and speed features that are fed into a FCM classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, only a limited number of studies have used geometric measurements such as width, length, height, volume, angle size, and area for classification purposes ( 24 – 31 ). Among those using clustering techniques, Javadi et al ( 24 ) classify vehicles into “private car,”“light trailer,”“lorry or bus,” and “heavy trailer,” using dimension and speed features that are fed into a FCM classifier. Their method reaches an accuracy of 96.5% on a data set with 400 vehicle images taken on a major highway.…”

Section: Literature Reviewmentioning

confidence: 99%

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Vehicle Dimensions Based Passenger Car Classification using Fuzzy and Non-Fuzzy Clustering Methods

Niroomand

Bach

Elser

2021

Transportation Research Record

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There has been globally continuous growth in passenger car sizes and types over the past few decades. To assess the development of vehicular specifications in this context and to evaluate changes in powertrain technologies depending on surrounding frame conditions, such as charging stations and vehicle taxation policy, we need a detailed understanding of the vehicle fleet composition. This paper aims therefore to introduce a novel mathematical approach to segment passenger vehicles based on dimensions features using a means fuzzy clustering algorithm, Fuzzy C-means (FCM), and a non-fuzzy clustering algorithm, K-means (KM). We analyze the performance of the proposed algorithms and compare them with Swiss expert segmentation. Experiments on the real data sets demonstrate that the FCM classifier has better correlation with the expert segmentation than KM. Furthermore, the outputs from FCM with five clusters show that the proposed algorithm has a superior performance for accurate vehicle categorization because of its capacity to recognize and consolidate dimension attributes from the unsupervised data set. Its performance in categorizing vehicles was promising with an average accuracy rate of 79% and an average positive predictive value of 75%.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Vehicle Dimensions Based Passenger Car Classification using Fuzzy and Non-Fuzzy Clustering Methods

Niroomand

Bach

Elser

2021

Transportation Research Record

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“…The five algorithms are variants k-means algorithm, i.e., k-means with cityblock distance, cosine similarity, correlation coefficient and hamming distance. The other two comparative algorithms include k-medoids [41] and fuzzy c-means clustering [30]. We have considered those algorithms which have to be provided with a cluster number in the beginning as our proposed approach requires a similar cluster number specification.…”

Section: Comparative Analysis and Discussionmentioning

confidence: 99%

An Evolutionary GA-Based Approach for Community Detection in IoT

et al. 2019

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Identifying traffic congestion and solving them by using predictive models has been ongoing research in intelligent transportation scenarios. However, it is improper that such scenarios can be judged on the basis of mean traffic intensity and mean traffic speed. This paper works on this aspect and uses data mining approaches to derive the aggregation metrics of traffic intensity data from the city of Madrid. This work uses a novel similarity measure by utilizing the results of the Wilcoxon Signed Rank test across 2018 locations to discover similarities. We propose a Genetic Algorithm on the results of the Wilcoxon test for forming communities based on the aggregation metrics. This work also compares and evaluates the performance of the proposed algorithm against standard distance measures and other state-of-the-art approaches. For finding the optimal number of possible communities in the data, we have taken the help of Davies -Bouldin Test. Our experimental results show the effectiveness of the Genetic Algorithm using various parameters, such as number of dissimilar points within a cluster, minimum number of dissimilar data points between clusters and overall based on Modified Silhouette coefficient. Furthermore, we find that our method is able to distribute the data points in a more uniform manner across formed communities in comparison to other approaches considered in this work.

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“…One of those challenges is to classify visually similar vehicles. Javadi et al propose to apply the fuzzy c-means (FCM) clustering [96] based on vehicle speed as an additional feature to address this challenge [76]. Specifically, they exploit the prior knowledge about varying traffic regulations and vehicle speeds to enhance the classification accuracy for the vehicles with similar dimensions.…”

Section: Over-roadway-based Vehicle Classificationmentioning

confidence: 99%

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Won

2020

IEEE Access

146

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A traffic monitoring system is an integral part of Intelligent Transportation Systems (ITS). It is one of the critical transportation infrastructures that transportation agencies invest a huge amount of money to collect and analyze the traffic data to better utilize the roadway systems, improve the safety of transportation, and establish future transportation plans. With recent advances in MEMS, machine learning, and wireless communication technologies, numerous innovative traffic monitoring systems have been developed. In this article, we present a review of state-of-the-art traffic monitoring systems focusing on the major functionality-vehicle classification. We organize various vehicle classification systems, examine research issues and technical challenges, and discuss hardware/software design, deployment experience, and system performance of the vehicle classification systems. Finally, we discuss a number of critical open problems and future research directions in an aim to provide valuable resources to academia, industry, and government agencies for selecting appropriate technologies for their traffic monitoring applications.

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Vehicle Classification Based on Multiple Fuzzy C-Means Clustering Using Dimensions and Speed Features

Cited by 21 publications

References 9 publications

Vehicle Dimensions Based Passenger Car Classification using Fuzzy and Non-Fuzzy Clustering Methods

Vehicle Dimensions Based Passenger Car Classification using Fuzzy and Non-Fuzzy Clustering Methods

An Evolutionary GA-Based Approach for Community Detection in IoT

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

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