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Traffic Incident Recognition Using Empirical Deep Convolutional Neural Networks Model

Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 217)

Abstract

Traffic incident detection plays an important role for a broad range of intelligent transport systems and applications such as driver- assistant, accident warning, and traffic data analysis. The primary goal of traffic incident detection systems in real-world is to identify traffic violations happening on the road in real-time. Although research community has made a significant attempt for detecting on-road violations, there are still challenges such as poor performance under real-world circumstances and real-time detection. In this paper, we propose a novel method which utilizes the powerful deep convolutional neural networks for vehicle recognition task to detect traffic events on the separate lane. Experimental results on real-world dataset videos as well as live stream in real-time from digital cameras demonstrate the feasibility and effectiveness of the proposed method for identifying incidents under various conditions of urban roads and highways.

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Correspondence to Nam Vu .

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Vu, N., Pham, C. (2018). Traffic Incident Recognition Using Empirical Deep Convolutional Neural Networks Model. In: Cong Vinh, P., Ha Huy Cuong, N., Vassev, E. (eds) Context-Aware Systems and Applications, and Nature of Computation and Communication. ICTCC ICCASA 2017 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-77818-1_9

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  • DOI: https://doi.org/10.1007/978-3-319-77818-1_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77817-4

  • Online ISBN: 978-3-319-77818-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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