Advertisement

Distributed Video Antifire Surveillance System Based on IoT Embedded Computing Nodes

  • Alessio GagliardiEmail author
  • Sergio Saponara
Conference paper
  • 14 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 627)

Abstract

This paper shows the design and the implementation of a distributed video antifire surveillance system based on Raspberry Pi embedded computing board and RPi Camera, able to detect the smoke and trigger autonomously a fire alarm. These smart cameras will be placed in different areas under surveillance, connected together according to an IoT-scheme via wired (e.g. ethernet) or wireless (e.g. Wi-Fi) links, and accessible to several users via web browser. A centralized web interface node shows the video stream of each camera in real time, while a video processing algorithm is responsible for the smoke identification and for the decision making of a fire alarm. Furthermore, the system is able to auto record the video in case of fire alarm. Target applications are distributed smoke/fire alarms in smart cities or smart transport systems or smart factories.

Keywords

IoT (Internet of Things) Distributed smoke/fire alarm systems Embedded video processing Raspberry pi 

References

  1. 1.
    Crisan C (2017) MotionEyeOs 1 Jan 2017 (Online). Available: https://github.com/ccrisan/motioneyeos/wiki
  2. 2.
    RPi-Cam-Web-Interface 29 Jan 2016 (Online). Available: https://elinux.org/RPi-Cam-Web-Interface
  3. 3.
    Vijayalakshmi S, Muruganand S (2017) Smoke detection in video images using background subtraction method for early fire alarm system. In: 2nd international conference on communication and electronics systems (ICCES), Coimbatore, pp 167–171Google Scholar
  4. 4.
    Tao C, Zhang J, Wang P (2016) Smoke detection based on deep convolutional neural networks. In: International conference on industrial informatics-computing technology, intelligent technology, industrial information integration (ICIICII), pp 150–153Google Scholar
  5. 5.
    Muhammad K et al (2018) Convolutional neural networks based fire detection in surveillance videos. IEEE Access 6Google Scholar
  6. 6.
    Filonenko A et al (2018) Fast smoke detection for video surveillance using CUDA. IEEE Trans Ind Inform 14(2):725–733CrossRefGoogle Scholar
  7. 7.
    Raspberry Pi Foundation (Online). Available: https://www.raspberrypi.org/
  8. 8.
    RPI Camera Module v.1.3 (Online). Available: https://www.raspberrypi.org/documentation/hardware/camera/
  9. 9.
    Saponara S, Gagliardi A (2019) AdViSED: Advanced Video SmokE Detection for real-time measurements in antifire surveillance indoor and outdoor systems. IEEE Trans Instrum MeasGoogle Scholar
  10. 10.
    ctypes—A Foreign function for Python—Documentation (Online). Available: https://docs.python.org/3/library/ctypes.html
  11. 11.
    Flask—Web development, one drop at time—Documentation (Online). Available: https://flask.palletsprojects.com/en/1.0.x/
  12. 12.
    Semantic UI—Documentation (Online). Available: https://semantic-ui.com/
  13. 13.
    SQLiteAlchemy—The Python SQL Toolkit and Object Relational Mapper—Documentation (Online). Available: https://www.sqlalchemy.org/
  14. 14.
    SQLite—Documentation (Online). Available: https://www.sqlite.org/docs.html
  15. 15.
    OpenCV 3.4.0—Open Source Computer Vision—Documentation (Online). Available: https://docs.opencv.org/3.4.0/

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Information EngineeringUniversity of PisaPisaItaly

Personalised recommendations