AI Based Smart Surveillance System

Authors

  • J Bhavyasri  Department of ECE, Kuppam Engineering College, Kuppam, Andhra Pradesh, India
  • Dr. G N Kodanda Ramaiah  Department of ECE, Kuppam Engineering College, Kuppam, Andhra Pradesh, India
  • Dr. K Rasadurai  Department of ECE, Kuppam Engineering College, Kuppam, Andhra Pradesh, India

DOI:

https://doi.org/10.32628/IJSRSET229672

Keywords:

Artificial Intelligence, Machine Learning, Computer Vision, Predictive Analysis, Object Detection, Behavioural Analysis.

Abstract

The AI Based smart surveillance system is gaining huge attention because of rise in demand for safety and security. The surveillance system is designed to analyse the video, image, and audio or any kind of surveillance data automatically without any human involvement. The developments that happened in recent years in the computer vision, sensor devices and Auto ML is playing a keen role in accrediting such intelligent system. There are many kinds of surveillance and security systems present in the market, but there is no live decision making and predictive analysis surveillance i.e., automated self-decision making system which helps the different public departments like police, health, fire and many more to track and reach the particular location where the incident is happened. In this proposed project we provide the AI based intelligent surveillance and security system which analyses and takes the decision immediately by itself based on the present parametric conditions as per the modules trained by us, It helps in providing the alerts quicker compared to the traditional alert system. The system which is proposed here will react automatically based on the situations occurs as it is trained in various modes. This paper is intending to provide the generalised outline of the AI based smart surveillance system and its functionalities. This paper also consists of information regarding the core processing steps of AI based surveillance system such as tracking, object detection and classification, and behavioural analysis.

References

  1. N. Sulman, T. Sanocki, D. Goldgof and R. Kasturi, How effective is human video surveillance performance?, 19th International Confer ence on Pat tern Recognition, Tampa, FL, 2008, pp.1-3R.
  2. R.J. Radke, S. Andra, O. Al-Kofahi and B. Roysam, Image change detection algorithms: A systematic survey, IEEE Trans. Image Process. 14 (2005) 294–307.
  3. M. Cristani, M. Farenzena, D. Bloisi and V. Murino, Background Subtraction for Automated Multisensor Surveillance: A Compreh en sive Review, EURASIP Journal on Advances in Signal Processing 2010 (2010) 343057.
  4. T. Bouwmans, F. El Baf, and B. Vachon, Statistical background modeling for foreground detection: A survey, Handbook of Pat tern Recognit ion and Computer Vision (Volume 4). Singapore: World Scient ific, 2010.
  5. T. Bouwmans, Recent advanced statistical backgro und mod el in g fo r foreground detection: A systematic survey, Recent Patents Comput . Sci. 4 (2011) 147–176.
  6. S. Brutzer, B. Hoferlin, G. Heidemann, Evaluation of background subtraction techniques for video surveillance, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Sp r in gs, USA, 2011, pp. 1937-1944.
  7. A. Sobral, A.Vacavant , A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos, Computer Vision and Image Understanding 122 (2014) 4-21.
  8. M. Paul, S. M. Haque and S. Chakraborty, Human detection in surveillance videos and its applications-a review, EURASIP Journal onar Advances in Signal Processing 2013(2013) 1-16.
  9. M. Enzweiler and D. M. Gavrila, Monocular pedestrian detection: Survey and experiments, IEEE Trans. Pattern An aly sis an d Machine Intelligence 31 (2009) 2179-2195.
  10. P. Dollar, C. Wojek, B. Schiele and P. Perona, Pedestrian Detection: An Evaluation of the State of the Art, IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (2012) 743-761.
  11. A. Yilmaz and M. Shah, Object tracking: A survey, Journal ACM Comput ing Surveys 38 (2006) article 13 (45 pages).
  12. A.W.M. Smeulders, Dung M. Chu, R.Cucchiara, S. Calderara, A. Dehghan and M. Shah, Visual Tracking: an Experimental Survey, IEEE Transaction on Pattern Analysis and Machine Intel l igence 36 ( 2 014 ) 1442-1468.
  13. A. Bedagkar-Gala and Shishir K. Shah, A survey of approaches and trends in person re-identification, Image and Vision Comput ing 32 (2014) 270-286.
  14. Y. Wu, J Lim and M Yang, Object Tracking Benchmark, IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (2 015 ) 1834-1848.
  15. T. Ko, A survey on behavior analysis in video surveillance for homeland security applications, 37th IEEE Applied Imagery Pattern Reco gn i t ion Workshop, Washington, DC, USA, 2008, pp.1-8.
  16. M. Cristani, R. Raghavendra, A. Del Bue and V. Murino, Human behavior analysis in video surveillance: A Social Signal Processing perspective, Neurocomput ing 100 (2013) 86-97.
  17. D. Gowshikaa, S Abirami and R Baskaran, Automated Human Behaviour Analysis from Surveillance videos: A survey, Art ificial Intelligence Review 42 (2014) 747-765.
  18. R. Gade and T. B. Moeslund, Thermal cameras and applications: A survey, Machine Vision & Applications 25 (2014) 245-262.
  19. A. Javed, A. Ejaz, S. Liaqat , A. Ashraf and M.B. Ihsan, Automatic target classifier for a Ground Surveillance Radar using lin ear d iscrimina nt analysis and Logistic regression, Radar Conference (EuRAD), 2012 9 th European, Amsterdam, 2012, pp.302-305.
  20. D. Kocur, P. Kazimir, J. Fortes, D. Novak, M. Drutarovsky, P. Galajda and R. Zet ik, Short-range UWB radar: Surveillance robot equipment o f the future, IEEE Internat ional Conference on Systems, Man and Cybernet ics (SMC), San Diego, CA, USA, 2014, pp.3767-3772.
  21. L. Spinello, K. O. Arras, R. Triebel and R. Siegwart , A layered approach to people detection in 3D range data, AAAI Conference on Ar t i f icial Intelligence (AAAI-10), Atlanta, Georgia, USA, 2010.
  22. L. Spinello, M. Luber and K.O. Arras, Tracking people in 3D u sing a bottom-up top-down detector, 2011 IEEE International Co n feren ce on Robot ics and Automation (ICRA), Shanghai, 2011, pp.1304-1310.
  23. Csaba Benedek, 3D people surveillance on range data seq uences o f a rotating Lidar, Pattern Recognition Letters 50 (2014) 149-158.
  24. Benny Ping Lai Lo, Jie Sun and Sergio A.Velast in, Fusing Visu a l a nd Audio Information in a Distributed Intelligent Surveillance S ystem fo r Public Transport Systems, Acta Automatica Sinica, 29 (2003) 393-407.
  25. D.L. Hall and J. Llinas, An introduction to multisensor data fusion, Proceedings of the IEEE 85 (1997) 6-23.
  26. E. F. Nakamura, A. A. F. Laureiro and A. C. Frery, Informat ion fu sion for wireless sensor networks: Methods, models, and classifications, ACM Comput . Surveys 39 (2007) 1–55.
  27. E.I. Gokce, A.K. Shrivastava, Jung Jin Cho and Yu Ding, Decision Fusion from Heterogeneous Sensors in Surveillance S en sor S ystems, IEEE Transactions on Automation Science and Engineerin g 8 (2 011 ) 228-233.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2022-12-30

Issue

Volume 10, Issue 1, January-February-2023

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
J Bhavyasri, Dr. G N Kodanda Ramaiah, Dr. K Rasadurai "AI Based Smart Surveillance System " International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 1, pp.10-15, January-February-2023. Available at doi : https://doi.org/10.32628/IJSRSET229672