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Video Image Recognition and Early Warning Algorithm of Public Security Prevention and Control Based on Deep Learning

  • Qing Lu
  • Xuchong LiuEmail author
  • Zhaohui Jiang
Conference paper
  • 7 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 551)

Abstract

As we all know, video surveillance plays an increasingly important role in the prevention and treatment of major public security cases (departments) and the maintenance of public security and other social security prevention and control work. At present, the research on public security prevention and control in the environment is still relatively backward. Because there are many cameras and large amount of image data, it is difficult to automatically identify image data. Based on the above reasons, this paper uses deep learning method to identify and warn video images to achieve automatic recognition. The goal of the research method of this paper is to combine the mathematical analysis of the existing deep learning model and summarize the characteristics of the deep learning method in the theoretical model. A character image recognition algorithm based on extended nonlinear kernel residual network is proposed. An image retrieval algorithm based on extended nonlinear kernel residual network and hash is proposed.

Keywords

Public security prevention and control Image identification Deep learning Feature extraction Search image Extended nonlinear kernel residual network 

Notes

Acknowledgments

This research was supported by Innovative Project of Science and Technology Planning Application of Ministry of Public Security (2018YYCXHNST048), Scientific Research Excellent Youth Project of Hunan Education Department (18B549), Science and Technology Project of Hunan Public Security Department, Hunan Natural Science Foundation (2018J2108), Hunan Social Science Foundation (17YBA151), Hunan Education Department Innovation Platform Open Fund (15K037), The Science and Technology Project of Hunan Province of China under Grant (2017SK1040).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Hunan Police AcademyChangshaChina
  2. 2.Hunan Provincial Key Laboratory of Network Investigational TechnologyHunanChina
  3. 3.Public Security Department of Hunan ProvinceHunanChina

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