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Context-Aware Systems and Applications, and Nature of Computation and Communication. 6th International Conference, ICCASA 2017, and 3rd International Conference, ICTCC 2017, Tam Ky, Vietnam, November 23-24, 2017, Proceedings

Research Article

Traffic Incident Recognition Using Empirical Deep Convolutional Neural Networks Model

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  • @INPROCEEDINGS{10.1007/978-3-319-77818-1_9,
        author={Nam Vu and Cuong Pham},
        title={Traffic Incident Recognition Using Empirical Deep Convolutional Neural Networks Model},
        proceedings={Context-Aware Systems and Applications, and Nature of Computation and Communication. 6th International Conference, ICCASA 2017, and 3rd International Conference, ICTCC 2017, Tam Ky, Vietnam, November 23-24, 2017, Proceedings},
        proceedings_a={ICCASA \& ICTCC},
        year={2018},
        month={3},
        keywords={Convolutional neural network Traffic incident Vehicle detection},
        doi={10.1007/978-3-319-77818-1_9}
    }
    
  • Nam Vu
    Cuong Pham
    Year: 2018
    Traffic Incident Recognition Using Empirical Deep Convolutional Neural Networks Model
    ICCASA & ICTCC
    Springer
    DOI: 10.1007/978-3-319-77818-1_9
Nam Vu1,*, Cuong Pham1,*
  • 1: Posts and Telecommunications Institute of Technology
*Contact email: namvh@ptit.edu.vn, cuongpv@ptit.edu.vn

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.

Keywords
Convolutional neural network Traffic incident Vehicle detection
Published
2018-03-16
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-319-77818-1_9
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