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Video Vehicle Detection and Recognition Based on MapReduce and Convolutional Neural Network

  • Mingsong Chen
  • Weiguang WangEmail author
  • Shi Dong
  • Xinling Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)

Abstract

With the rapid growth of traffic video data, it is necessary to improve the computing power and accuracy of image processing. In this paper, a video vehicle detection and recognition system based on MapReduce and convolutional neural network is proposed to reduce the time-consume and improve the recognition accuracy in video analysis. First, a fast and reliable deep learning algorithm based on YOLOv2 is used to detect vehicle in real-time. And then the license plate recognition algorithm based on improved convolutional neural network is presented to recognize the license plate image extracted from the detected vehicle region. Finally, the Hadoop Video Processing Interface (HVPI) and MapReduce framework are combined to apply the video vehicle detection and recognition algorithms for parallel processing. Experimental results are presented to verify that the proposed scheme has advantages of high detection rate and high recognition accuracy, and strong ability of data processing in large-scale video data.

Keywords

YOLOv2 MapReduce Convolutional neural network HVPI 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61561014) and the Innovation Project of GUET Graduate Education (No. 2016YJCX93 & No. 2018YJCX29).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mingsong Chen
    • 1
  • Weiguang Wang
    • 1
    Email author
  • Shi Dong
    • 1
  • Xinling Zhou
    • 1
  1. 1.Guilin University of Electronic TechnologyGuilinChina

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