Robust Vehicle Classification Based on the Combination of Deep Features and Handcrafted Features

Jiang, Liying; Li, Jiafeng; Li, Zhuo; Zhu, Ziqi

doi:10.1109/trustcom/bigdatase/icess.2017.323

Cited by 9 publications

(7 citation statements)

References 8 publications

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“…The classification method proposed in this paper is a new semi-supervised clustering scheme SSFCM that incorporates semi-supervised information in FCM algorithm to considerably improve its effectiveness [22,[62][63][64][65][66][67][68][69]. More details about the feature learning techniques can be found in the article by Jiang et al [70], in which they combined several feature extraction methods with a support vector machine classifier to group the vehicles in six categories, namely "large bus", "passenger car", "motorcycle", "minibus", "truck" and "van". This study achieved a classification accuracy of 97.4%.…”

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%

“…Moussa ( 29 ) used geometric-based and appearance-based features in a supervised learning model (support vector network) for multi-class (small, medium, and large size) and intra-class (pickup, sport utility vehicle, and van) vehicle classification. Jiang et al ( 30 ) combined several feature extraction methods with a support vector machine classifier to group the vehicles in six categories (large bus, car, motorcycle, minibus, truck, and van) and achieved a classification accuracy of 97.4%. Lastly, Cheung et al ( 31 ) proposed a vehicle classification method based on magnetic sensors to classify vehicles into six types (passenger vehicle, SUV, van, bus, mini-trucks, truck).…”

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: Journal of the Transportation R

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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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“…ough the deep feature-based approaches can enhance the accuracy of vehicle classification effectively, these methodologies need a huge amount of data to achieve significant accuracy in real-time ITS applications [26][27][28][29]. In the recent era, extensive research has been carried out in this field; however, the available public datasets for self-driving vehicles/intelligent transportation systems comprise modern vehicle types, which are common in well developed countries.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems

et al. 2021

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In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the above-mentioned issues in traditional methods. However, convolutional neural networks require piles of data including noise, weather, and illumination factors to ensure robustness in real-time applications. Moreover, there is no generalized dataset available to validate the efficacy of vehicle classification systems. To overcome these issues, we propose a convolutional neural network-based vehicle classification system to improve robustness of vehicle classification in real-time applications. We present a vehicle dataset comprising of 10,000 images categorized into six-common vehicle classes considering adverse illuminous conditions to achieve robustness in real-time vehicle classification systems. Initially, pretrained AlexNet, GoogleNet, Inception-v3, VGG, and ResNet are fine-tuned on self-constructed vehicle dataset to evaluate their performance in terms of accuracy and convergence. Based on better performance, ResNet architecture is further improved by adding a new classification block in the network. To ensure generalization, we fine-tuned the network on the public VeRi dataset containing 50,000 images, which have been categorized into six vehicle classes. Finally, a comparison study has been carried out between the proposed and existing vehicle classification methods to evaluate the effectiveness of the proposed vehicle classification system. Consequently, our proposed system achieved 99.68%, 99.65%, and 99.56% accuracy, precision, and F1-score on our self-constructed dataset.

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Robust Vehicle Classification Based on the Combination of Deep Features and Handcrafted Features

Cited by 9 publications

References 8 publications

Robust Vehicle Classification Based on Deep Features Learning

Robust Vehicle Classification Based on Deep Features Learning

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

Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems

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