Classification of Vehicle Make Based on Geometric Features and Appearance-Based Attributes Under Complex Background

Arunkumar, K.; Danti, Ajit; Manjunatha, Hemanth

doi:10.1007/978-981-13-9181-1_4

Cited by 11 publications

(3 citation statements)

References 7 publications

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“…Zhang et al [51] developed a length-based method for vehicle detection and classification, reaching an accuracy of 97% for truck classification. Arunkumar et al [52] used a neural network classifier based on geometrical features and appearance-based attributes to classify passenger vehicles into brands. Moussa [53] also used geometric-based and appearance-based features for multi-VOLUME XX, 20XX class ("small", "medium", and "large size") and intra-class ("pickup", "sport utility vehicle", and "van") vehicle classification using a support-vector network mod-el.…”

Section: Related Workmentioning

confidence: 99%

Robust Vehicle Classification Based on Deep Features Learning

2021

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This paper aims to introduce a scientific Semi-Supervised Fuzzy C-Mean (SSFCM) clustering approach for passenger cars classification based on the feature learning technique. The proposed method is able to classify passenger vehicles in the micro, small, middle, upper middle, large and luxury classes. The performance of the algorithm is analyzed and compared with an unsupervised fuzzy C-means (FCM) clustering algorithm and Swiss expert classification dataset. Experiment results demonstrate that the classification of SSFCM algorithm had better correlation with expert classification than traditional unsupervised algorithm. These results exhibit that SSFCM can reduce the sensitivity of FCM to the initial cluster centroids with the help of labeled instances. Furthermore, SSFCM results in improved classification performance by using the resampling technique to deal with the multi-class imbalanced problem and eliminate the irrelevant and redundant features.

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Section: Related Workmentioning

confidence: 99%

Robust Vehicle Classification Based on Deep Features Learning

2021

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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%

“…They reported 97% accuracy for truck classification. Arunkumar et al ( 28 ) designed a novel approach to classify vehicles into their brands using geometrical features and appearance-based attributes. Based on this, they are able classify the vehicles into different classes of models that belong to the same brand using a neural network classifier.…”

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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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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