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

Saleh Javadi | Zendy; Muhammad Rameez | Zendy; Mattias Dahl | Zendy; Mats I. Pettersson | Zendy

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

Author(s) -

Saleh Javadi,

Muhammad Rameez,

Mattias Dahl,

Mats I. Pettersson

Publication year - 2018

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2018.08.085

Subject(s) - computer science , cluster analysis , partition (number theory) , initialization , exploit , fuzzy logic , data mining , class (philosophy) , task (project management) , fuzzy clustering , artificial intelligence , pattern recognition (psychology) , machine learning , mathematics , computer security , management , combinatorics , economics , programming language

Vehicle classification has a significant use in traffic surveillance and management. There are many methods proposed to accomplish this task using variety of sensors. In this paper, a method based on fuzzy c-means (FCM) clustering is introduced that uses dimensions and speed features of each vehicle. This method exploits the distinction in dimensions features and traffic regulations for each class of vehicles by using multiple FCM clusterings and initializing the partition matrices of the respective classifiers. The experimental results demonstrate that the proposed approach is successful in clustering vehicles from different classes with similar appearances. In addition, it is fast and efficient for big data analysis.

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