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Smart Physical Intruder Detection System for Highly Sensitive Area

  • Smita KasarEmail author
  • Vivek Kshirsagar
  • Sagar Bokan
  • Ninad Rathod
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 165)

Abstract

In this ever-growing world of automation and digitization, where data is a pivotal element for the growth of every individual, institution, and organization, whether digital or physical, data could also be the reason for destruction, if acquired by an antagonist through unconventional access. Data is a very sensitive point in all the domains ranging from an individual’s personal space to tactical military centers such as defense institutions, military matters, financial institutions, hospitals, and educational institutions. Thus, it is necessary to protect the data from intruders. Physical Intruder Detection is equally important as the detection of intrusion in computer networks. Though the later is always digital and without manual intervention. Physical Intruder Detection can be either digital or done manually. The paper presents a system for an enclosed area, based on IoT and supported by Digital Image Processing, to capture any Physical Intruder who breaches the security system and alert the rightful person regarding the intrusion. The approach uses the PIR motion sensor to detect any suspicious activity, turn on the webcam and with the help of Face Recognition System using Digital Image Processing, recognizes whether it is the rightful person or not. If it is an Intruder, then the webcam will start recording the activity of the Intruder and send a text message as well as an email to the system owner alerting him/her/them about the intrusion. A link to this live feed to the system owner is also attached to the alert message and mail. This Intruder Detection System is energy efficient as well because the webcam will be turned on only when the motion sensor detects any suspicious activity.

Keywords

Safety IoT Intruder detection Digital facial recognition Hadoop distributed file system 

References

  1. 1.
    Sukmana, H.T., Farisi, M.G., Khairani, D.: Prototype utilization of PIR motion sensor for real time surveillance system and web-enabled lamp automation. In: IEEE Asia Pacific Conference on Wireless and Mobile (2015)Google Scholar
  2. 2.
    Bartlett, M.S., Movellan, J.R., Sejonowski, T.J.: Face recognition by independent component analysis. IEEE Trans. Neural Netw. 13(6), 1450–1464 (2002)CrossRefGoogle Scholar
  3. 3.
    Oludele, A., Ayodele, O., Oladele, O., Olurotimi, S.: Design of an automated intrusion detection system incorporating an alarm. J. Comput. ISSN: 2151-9617 (2009)Google Scholar
  4. 4.
    Kodali, R.K., Soratkal, S.R.: MQTT based home automation system using ESP8266. In: IEEE Region 10 Humanitarian Technology Conference (2016)Google Scholar
  5. 5.
    Freer, J.A., Beggs, B.J., Fernandez-Canque, H.L., Chevrier, F., Goryashko, A.: Automatic video surveillance with intelligent scene monitoring and intruder detection. ECOS, pp. 109–133 (1997).  https://doi.org/10.1049/cp:19970433
  6. 6.
    Monzo, D., Albiol, A., Albiol, A., Mossi, J.M.: A comparative study of facial landmark localization methods for face recognition using HOG descriptors. In: Proceedings of IEEE (2010)Google Scholar
  7. 7.
    Jayant: PIR sensor based motion detector/sensor circuit. (2015) Retrieved from https://circuitdigest.com/electronic-circuits/pir-sensor-based-motion-detector-sensor-circuit
  8. 8.
    Nguyen, H.-Q., Loan, T.T.K., Mao, B.D., Huh, E.-N.: Low Cost Real-Time System Monitoring Using Raspberry Pi. Computer Engineering Department, Kyung Hee University, Yongin, South Korea (2015)Google Scholar
  9. 9.
    Suganthy, M., Ramamoorthy, P.: Principal component analysis based feature extraction, morphological edge detection and localization for fast iris recognition. J. Comput. Sci. 8(9), 1428–1433, ISSN 1549-3636 (2012)Google Scholar
  10. 10.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  11. 11.
    Ming, Y., Ruan, Q., Li, X., Mu, M.: Efficient kernel discriminate spectral regression for 3D face recognition. In: Proceedings of ICSP 2010 (2010)Google Scholar
  12. 12.
    Huang, L.-L., Shimizu, A., Hagihara, Y., Kobatake, H.: Face Detection from Cluttered Images Using a Polynomial Neural Network. Elsevier Science (2002)Google Scholar
  13. 13.
    Paul, L.C., Suman, A.A., Sultan, N.: Methodological analysis of principal component analysis (PCA) method. Int. J. Comput. Eng. Manag. 16(2), 32–38 (2013)Google Scholar
  14. 14.
    Castells, F., Laguna, P., Sornmo, L., Bollmann, A., Roig, J.M.: Principal component analysis in ECG signal processing. EURASIP J. Adv. Signal Process. 2007, 1–21 (2007)Google Scholar
  15. 15.
    Jolliffe, I.T., Cadima, J.: Principal component analysis: a review and recent developments. Philos. Trans. A Math. Phys. Eng. Sci. 374(2065), 0150202. 13 Apr 2016 (2016)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K.: KNN Model-Based Approach in Classification, pp. 1–12Google Scholar
  17. 17.
    Imandoust, S.B., Bolandraftar, M.: Application of K-nearest neighbor (KNN) approach for predicting economic events: theoretical background. Int. J. Eng. Res. Appl. 3(5), 605–610 (2013)Google Scholar
  18. 18.
    Moise, I., Pournaras, E., Helbing, D.: K-Nearest Neighbour Classifier. ETH zurich (2015)Google Scholar
  19. 19.
    Vermeulen, J., Hillebrand, A., Geraerts, R.: A comparative study of k-nearest neighbor techniques in crowd simulation. In: 30th Conference on Computer Animation and Social Agents, 23 May 2017 (2017)CrossRefGoogle Scholar
  20. 20.
    Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The Hadoop distributed file system. In: IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST) (2010)Google Scholar
  21. 21.
    Aswinth Raj, B.: How to Send Text Message (SMS) Using ESP8266. (2003) Retrieved from https://circuitdigest.com/microcontroller-projects/sending-sms-using-esp8266

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringMaharashtra Institute of TechnologyAurangabadIndia
  2. 2.Department of Computer Science and MCAGovernment Engineering CollegeAurangabadIndia
  3. 3.NvidiaPuneIndia
  4. 4.TCSPuneIndia

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