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Research on the Deep Recognition of Urban Road Vehicle Flow Based on Deep Learning

Author

Listed:
  • Shuanfeng Zhao

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Chao Wang

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Pei Wei

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Qingqing Zhao

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

Abstract

At present, the recognition of vehicle flow is mainly achieved with an artificial statistical method or by intelligent recognition based on video. The artificial method requires a large amount of manpower and time, and the existing video-based vehicle flow recognition methods are only applicable to straight roads. Therefore, a deep recognition model (DERD) for urban road vehicle flow is proposed in this paper. Learning from the characteristic that the cosine distance between the feature vectors of the same target in different states is in a fixed range, we designed a deep feature network model (D-CNN) to extract the feature vectors of all vehicles in the traffic flow and to intelligently determine the real-time statistics of vehicle flow based on the change of distance between vectors. A detection and tracking model was built to ensure the stability of the feature vector extraction process and to obtain the behavior trajectory of the vehicle. Finally, we combined the behavior and the number of vehicle flows to achieve the deep recognition of vehicle flow. After testing with videos recorded in actual scenes, the experimental results showed that our method can intelligently achieve the deep recognition of urban road vehicle flow. Compared with the existing methods, our approach shows higher accuracy and faster real-time performance.

Suggested Citation

  • Shuanfeng Zhao & Chao Wang & Pei Wei & Qingqing Zhao, 2020. "Research on the Deep Recognition of Urban Road Vehicle Flow Based on Deep Learning," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:7094-:d:406609
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    References listed on IDEAS

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    2. Wei Zhou & Wei Wang & Xuedong Hua & Yi Zhang, 2020. "Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition," Sustainability, MDPI, vol. 12(15), pages 1-23, July.
    3. Xu Sun & Kun Lin & Pengpeng Jiao & Huapu Lu, 2020. "The Dynamical Decision Model of Intersection Congestion Based on Risk Identification," Sustainability, MDPI, vol. 12(15), pages 1-16, July.
    4. Robert Socha & Bogusław Kogut, 2020. "Urban Video Surveillance as a Tool to Improve Security in Public Spaces," Sustainability, MDPI, vol. 12(15), pages 1-12, August.
    5. Wei Yu & Hua Bai & Jun Chen & Xingchen Yan, 2019. "Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro," Sustainability, MDPI, vol. 11(18), pages 1-19, September.
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    Cited by:

    1. Andrzej Paszkiewicz & Bartosz Pawłowicz & Bartosz Trybus & Mateusz Salach, 2021. "Traffic Intersection Lane Control Using Radio Frequency Identification and 5G Communication," Energies, MDPI, vol. 14(23), pages 1-17, December.

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