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Current Datasets and Their Inherent Challenges for Automatic Vehicle Classification

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Machine Learning for Cyber Physical System: Advances and Challenges

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 60))

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Abstract

Automatic Vehicle Classification (AVC) systems have become a need of the hour to manage the ever-increasing number of vehicles on roads and thus maintain a well-organized traffic system. Researchers around the world have proposed several techniques in the last two decades to address this challenge. However, these techniques should be implemented on realistic datasets to evaluate their efficiency in practical situations. Hence, it is understood that for the success of this domain, datasets play an important role, mostly publicly accessible by the research community. This article presents a comprehensive survey regarding various datasets available for solving AVC problems such as vehicle make and model recognition (VMMR), automatic license plate recognition, and vehicle category identification during the last decade. The datasets are categorized into two types: still image-based, and video-based. Again, the still image-based datasets are further classified as aerial imagery-based and front image-based datasets. This study has presented a thorough comparison of the different types of datasets with special reference to their characteristics. This study also provides an elaborative analysis of all the datasets and suggests a few fundamental future research scopes toward AVC. This survey can act as a preliminary guideline for researchers to develop a robust AVC system specially designed as per their needs and also to choose suitable datasets for comparing their models.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India

    Sourajit Maity & Ram Sarkar

  2. Department of Information Technology, Jadavpur University, Kolkata, 700106, India

    Pawan Kumar Singh

  3. Department of Automation and Control Processes, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russian Federation, 197022

    Dmitrii Kaplun

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  1. Sourajit Maity
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  2. Pawan Kumar Singh
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  3. Dmitrii Kaplun
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  4. Ram Sarkar
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Correspondence to Pawan Kumar Singh .

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Editors and Affiliations

  1. Department of Computer Science, MSCB University, Baripada, India

    Janmenjoy Nayak

  2. Department of Computer Application, Veer Surendra Sai University of Technology, Sambalpur, India

    Bighnaraj Naik

  3. Department of Artificial Intelligence and Data Science, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Vimal S

  4. Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Margarita Favorskaya

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Maity, S., Singh, P.K., Kaplun, D., Sarkar, R. (2024). Current Datasets and Their Inherent Challenges for Automatic Vehicle Classification. In: Nayak, J., Naik, B., S, V., Favorskaya, M. (eds) Machine Learning for Cyber Physical System: Advances and Challenges. Intelligent Systems Reference Library, vol 60. Springer, Cham. https://doi.org/10.1007/978-3-031-54038-7_14

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