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  • Open Access


    Convolutional Neural Network for Overcrowded Public Transportation Pickup Truck Detection

    Jakkrit Suttanuruk1, Sajjakaj Jomnonkwao1,*, Vatanavong Ratanavaraha1, Sarunya Kanjanawattana2

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5573-5588, 2023, DOI:10.32604/cmc.2023.033900

    Abstract Thailand has been on the World Health Organization (WHO)’s notorious deadliest road list for several years, currently ranking eighth on the list. Among all types of road fatalities, pickup trucks converted into vehicles for public transportation are found to be the most problematic due to their high occupancy and minimal passenger safety measures, such as safety belts. Passenger overloading is illegal, but it is often overlooked. The country often uses police checkpoints to enforce traffic laws. However, there are few or no highway patrols to apprehend offending drivers. Therefore, in this study, we propose the use of existing closed-circuit television… More >

  • Open Access


    Lightning Search Algorithm with Deep Transfer Learning-Based Vehicle Classification

    Mrim M. Alnfiai*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6505-6521, 2023, DOI:10.32604/cmc.2023.033422

    Abstract There is a drastic increase experienced in the production of vehicles in recent years across the globe. In this scenario, vehicle classification system plays a vital part in designing Intelligent Transportation Systems (ITS) for automatic highway toll collection, autonomous driving, and traffic management. Recently, computer vision and pattern recognition models are useful in designing effective vehicle classification systems. But these models are trained using a small number of hand-engineered features derived from small datasets. So, such models cannot be applied for real-time road traffic conditions. Recent developments in Deep Learning (DL)-enabled vehicle classification models are highly helpful in resolving the… More >

  • Open Access


    A Particle Swarm Optimization Based Deep Learning Model for Vehicle Classification

    Adi Alhudhaif1,*, Ammar Saeed2, Talha Imran2, Muhammad Kamran3, Ahmed S. Alghamdi3, Ahmed O. Aseeri1, Shtwai Alsubai1

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 223-235, 2022, DOI:10.32604/csse.2022.018430

    Abstract Image classification is a core field in the research area of image processing and computer vision in which vehicle classification is a critical domain. The purpose of vehicle categorization is to formulate a compact system to assist in real-world problems and applications such as security, traffic analysis, and self-driving and autonomous vehicles. The recent revolution in the field of machine learning and artificial intelligence has provided an immense amount of support for image processing related problems and has overtaken the conventional, and handcrafted means of solving image analysis problems. In this paper, a combination of pre-trained CNN GoogleNet and a… More >

  • Open Access


    An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

    Shabana Habib1, Noreen Fayyaz Khan2,*

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3321-3335, 2021, DOI:10.32604/cmc.2021.015504

    Abstract Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used to quickly learn the features… More >

  • Open Access


    Multi-task Joint Sparse Representation Classification Based on Fisher Discrimination Dictionary Learning

    Rui Wang1, Miaomiao Shen1,*, Yanping Li1, Samuel Gomes2

    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 25-48, 2018, DOI:10.32604/cmc.2018.02408

    Abstract Recently, sparse representation classification (SRC) and fisher discrimination dictionary learning (FDDL) methods have emerged as important methods for vehicle classification. In this paper, inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection, we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors. To improve the classification accuracy in complex scenes, we develop a new method, called multi-task joint sparse representation classification based on fisher discrimination dictionary learning, for… More >

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