Skip to main content

Implementation of IoT-Based Smart Video Surveillance System

  • Conference paper
  • First Online:
Computational Intelligence in Data Mining

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 556))

Abstract

Smart video surveillance is a IOT-based application as it uses Internet for various purposes. The proposed system intimates about the presence of any person in the premises, also providing more security by recording the activity of that person. While leaving the premises, user activates the system by entering password. System working starts with detection of motion refining to human detection followed by counting human in the room and human presence also gets notified to neighbor by turning on alarm. In addition, notification about the same is send to user through SMS and e-mail. The proposed system’s hardware implementation is supported by Raspberry Pi and Arduino board; on the other hand, software is given by OpenCV (for video surveillance) and GSM module (for SMS alert and e-mail notification). Apart from security aspect, system is intelligent enough to optimize power consumption wastage if user forgets to switch off any electronic appliances by customizing coding with specific appliances.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Md. Syadus Sefat, Abdullah Al Mamun Khan, Md. Shahjahan “Implementation of vision based intelligent home automation and security system”, 3rd INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION 2014.

    Google Scholar 

  2. F. Bartolini, V. Cappellini and A. Mecocci, Counting people getting in and out of a bus by real time image-sequence processing, Image and Vision Computing, vol. 12, no. 1, Jan. 1994, pp. 36–41.

    Google Scholar 

  3. A. Albiol, I. Mora and V. Naranjo, Real-time high density people counter using morphological tools, IEEETrans. Intelligent Transportation Systems, vol. 2, no. 4, Dec. 2001, pp. 204–218.

    Google Scholar 

  4. M. Rossi and A. Bozzoli, Tracking and counting moving people, IEEE International Conference on Image Processing (ICIP), vol. 3, 1994, pp. 212–216.

    Google Scholar 

  5. G. Sexton, X. Zhang and G. Redpath, Advances in automatic counting of pedestrians, 1995 European Convention on Security and Detection (ECSD95), 1995, pp. 106–110.

    Google Scholar 

  6. K. Terada, D. Yoshida, S. Oe and J. Yamaguchi, A method of counting the passing people by using the stereo images, IEEE International Conference on Image Processing (ICIP), vol. 2, 1999, pp. 338–342.

    Google Scholar 

  7. O. Masoud and N. P. Papanikolopoulos, A novel method for tracking and counting pedestrians in real-time using a single camera, IEEE Trans. Vehicular Technology, vol. 50, no. 5, Sep. 2001, pp. 1267–1278.

    Google Scholar 

  8. J. W. Kim, K. S. Choi, B. D. Choi and S. J. Ko, Real-time vision-based people counting system for security door, International Technical Conference on Circuits/Systems Computers and Communications, 2002, pp. 1416–1419.

    Google Scholar 

  9. J. Bescos, J. M. Menendez and N. Garcia, DCT based segmentation applied to a scalable zenithal people counter, IEEE International Conference on Image Processing (ICIP), vol. 3, 2003, pp. 1005–1008.

    Google Scholar 

  10. T. H. Chen and C. W. Hsu, An automatic bi-Directional passing-people counting method based on color-image processing, 37th IEEE International Carnahan Conference on Security Technology, Taiwan, Oct. 2003, pp. 200–207.

    Google Scholar 

  11. KaewTraKulPong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection Proceedings 2nd European Workshop on Advanced Video Based Surveillance Systems (AVBS 2001), Kingston, UK, September 2001 3rd INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION.

    Google Scholar 

  12. Khot Harish S, Gote Swati R, Khatal Sonali B, Pandarge Sangmesh Smart Video Surveillance, IJ EERT Volume 3, Issue 1, January 2015, PP 109–112 ISSN 2349-4395 (Print) & ISSN 2349-4409 (Online).

    Google Scholar 

  13. U. RAMAKRISHNA, N. SWATHI: Design and Implementation of an IoT Based Smart Security Surveillance System, IJSETR ISSN 2319–8885 Vol. 05, Issue. 04, February-2016, Pages: 0697–0702.

    Google Scholar 

  14. Akshada Deshmukh, 2 Harshalata Wadaskar, 3 Leena Zade, 4Neha Dhakate, 5 Preetee Karmore: Webcam Based Intelligent Surveillance System, IJES Vol. 2, 8 March 2013, Pp 38–42 Issn(e): 2278-4721, Issn(p):2319-6483.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sonali P. Gulve .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Gulve, S.P., Khoje, S.A., Pardeshi, P. (2017). Implementation of IoT-Based Smart Video Surveillance System. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 556. Springer, Singapore. https://doi.org/10.1007/978-981-10-3874-7_73

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-3874-7_73

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3873-0

  • Online ISBN: 978-981-10-3874-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics