A vehicle classification system based on hierarchical multi-SVMs in crowded traffic scenes

Fu, Huiyuan; Ma, Huadóng; Liu, Yinxin; Lu, Dawei

doi:10.1016/j.neucom.2015.12.134

Cited by 39 publications

(15 citation statements)

References 11 publications

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“…The different extraction and segmentation methods produce a set of parameters for each vehicle: Speed, length, width and height of vehicle. The goal of the classification operation is to categorize the vehicle into a number of predefined types using these generated parameters (Fu et al, 2016;Zhou and Cheung, 2016;Wen et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling

Moutakki

Ouloul

Afdel

et al. 2017

Transport and Telecommunication Journal

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The scope of this paper is a video surveillance system constituted of three principal modules, segmentation module, vehicle classification and vehicle counting. The segmentation is based on a background subtraction by using the Codebooks method. This step aims to define the regions of interest associated with vehicles. To classify vehicles in their type, our system uses the histograms of oriented gradient followed by support vector machine. Counting and tracking vehicles will be the last task to be performed. The presence of partial occlusion involves the decrease of the accuracy of vehicle segmentation and classification, which directly impacts the robustness of a video surveillance system. Therefore, a novel method to handle the partial occlusions based on vehicle classification process have developed. The results achieved have shown that the accuracy of vehicle counting and classification exceeds the accuracy measured in some existing systems.

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Section: Related Workmentioning

confidence: 99%

Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling

Moutakki

Ouloul

Afdel

et al. 2017

Transport and Telecommunication Journal

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“…Great strides in automatic monitoring systems and ITSs are being made with the development of artificial intelligence technology [ 13 ]. In our previous work, a feature extraction and comparison method was presented for vehicle classification with a single magnetic sensor.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor

Chang

Wang

Bao

et al. 2018

Sensors

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This paper aims to improve the accuracy of automatic vehicle classifiers for imbalanced datasets. Classification is made through utilizing a single anisotropic magnetoresistive sensor, with the models of vehicles involved being classified into hatchbacks, sedans, buses, and multi-purpose vehicles (MPVs). Using time domain and frequency domain features in combination with three common classification algorithms in pattern recognition, we develop a novel feature extraction method for vehicle classification. These three common classification algorithms are the k-nearest neighbor, the support vector machine, and the back-propagation neural network. Nevertheless, a problem remains with the original vehicle magnetic dataset collected being imbalanced, and may lead to inaccurate classification results. With this in mind, we propose an approach called SMOTE, which can further boost the performance of classifiers. Experimental results show that the k-nearest neighbor (KNN) classifier with the SMOTE algorithm can reach a classification accuracy of 95.46%, thus minimizing the effect of the imbalance.

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“…At the same time, feature classifier pairs, called as an active learning framework in [10], has been widely used in vehicle detection under various conditions. The combination of HOG feature and SVM classifier, and Haar-like features together with Adaboost classifier, have achieved impressive results in vehicle detection and classification [11].…”

Section: Feature-based Methodsmentioning

confidence: 99%

Vehicle Detection Using Imaging Technologies and its Applications under Varying Environments: A Review

Yang¹

2017

World Congress on Civil, Structural, and Environmental Engineering

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Robust and efficient vehicle detection from images is an important task in Intelligent Transportation Systems. With the development of computer vision techniques and consequent accessibility of video image data, new applications have been enabled to on-road vehicle detection algorithms. This paper provides a review of the literature in vehicle detection under varying environments. Due to the variability of on-road driving environments, vehicle detection may face different problems and challenges. Therefore, many approaches have been proposed to detect vehicles, such as feature-based method and model-based methods. In addition, special illumination, weather and driving scenarios are discussed in terms of methodology and quantitative evaluation. In the future, works should be focused on robust vehicle detection approaches for various on-road conditions.

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A vehicle classification system based on hierarchical multi-SVMs in crowded traffic scenes

Cited by 39 publications

References 11 publications

Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling

Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling

Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor

Vehicle Detection Using Imaging Technologies and its Applications under Varying Environments: A Review

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