Suspect Detection System-An Architecture Based on Surveillance Visual Analytics using IoT

Authors

  • Anirudh Rodda Department of Computer Science and Engineering, CVR College of Engineering, Telangana, India Author
  • SugunaMallika S Department of Computer Science and Engineering, CVR College of Engineering, Telangana, India Author
  • M Jaiganesh Department of Computer Science and Engineering, CVR College of Engineering, Telangana, India Author
  • Mrunalini M Department of Master of Computer Applications, M S Ramaiah Institute of Technology, Bangalore, India. Author

DOI:

https://doi.org/10.61841/wn3w8f36

Keywords:

physical security, automated surveillance system, Visual Analytics

Abstract

In the present dayscenariothe issue of physical security is of utmost importance in any area. Providing an automated surveillance system to detectsuspicious personnel or activities withina particularvicinity with an alert feature is the need of the hour. The 21st century most prominent technology – Internet of Things (IoT) is being used in developing smart real time security surveillance systems, and providing enhanced performance and effective results by eliminating humansupervision to maximum extent. This new technology is effective both cost and storage wise. Here, the data can be transferred to a remote server such as cloud. Also, the user will be notified via e-mail after an unusual activity/movement is captured. Further, visual analytics helps to investigate the data captured and take decisions. This paper is intended to improve the understanding of related tool, technology and methodology used to design and implement such smart surveillance system systematically. In this paper, a methodology is proposed to capture the visitors’ image, identify the unknown visitors within the vicinity, storing the unknown visitors’data to the cloud server, processing the unknownvisitors’ logs and sending an e-mail notification to the security departmentof the suspicious visitor.

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References

1. André F. M. Batista, Pedro L. P. Correa, GiriPalanisamy,”Visual Analytics Improving Data Understandability in IoT Projects: An Overview of the U. S. DOE ARM Program Data Science Tools”, In Proceedings of the 13th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), IEEE, ISBN: 978-1-5090-2834-4, 2016.

2. Bowen Du ,Chuanren Liu, Wenjun Zhou , Zhenshan Hou, and Hui Xiong, “Detecting Pickpocket Suspects from Large-Scale Public Transit Records”, IEEE Transactions on Knowledge And Data Engineering, Vol. 31, NO. 3, March 2019.

3. Dmitry O. Gorodnichy and Tony Mungham, “Automated video surveillance: challenges and solutions. ACE Surveillance (Annotated Critical Evidence) case study”, NATO SET-125 Symposium "Sensor and Technology for Defence against Terrorism", Mainheim, April 2008.

4. Fahad Parvez Mahdi, Md. Mahmudul Habib, Md. Atiqur Rahman Ahad, Susan Mckeever, A.S.M.Moslehuddin and Pandian Vasant, “Face recognition-based real- time system for surveillance”, Intelligent Decision Technologies, ISSN 1872-4981/17,79-92, 2017.

5. Farah Deeba, Hira Memon, Fayaza Ali Dharejo, Aftab Ahmed, Abddul Ghaffar, “LBPH-based Enhanced Real-Time Face Recognition”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 5, 2019.

6. Gaocheng Liu, Shuai Liu, Khan Muhammad, Arun Kumar Sangaiah, And Faiyaz Doctor, “Object Tracking in Vary Lighting Conditions for Fog Based Intelligent Surveillance of Public Spaces”, Special Section on Real-Time Edge Analytics For Big Data In Internet of Things, Vol.6, 10.1109/ACCESS.2018.2834916.

7. Guo-Dao Sun, Ying-Cai Wu, Rong-Hua Liang and Shi-Xia Liu ,” A Survey of Visual Analytics Techniques and Applications” State-of-the-Art Research and Future Challenges, Journal of Computer Science and Technology, volume 28, pg: 852–867(2013).

8. https://pythonprogramming.net/raspberry-pi-camera-opencv-face-detection-tutorial/

9. IndrajitPatil , Saurabh Jaiswal , Pallavi Sakhare , Mohammad Shoaib , Asst. Prof. Poonam Gupta, “A Survey on IOT Based Security System”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, Issue 11, November 2016

10. Jing Wang ;Zhijie Xu , “Crowd anomaly detection for automated video surveillance”, In Proceedings of the 6th International Conference on Imaging for Crime Prevention and Detection (ICDP-15), ISBN: 978- 1-78561-131-5, 2015.

11. JoshilaGrace.L.K, K.Reshmi, “Face Recognition in Surveillance System”, IEEE Sponsored 2nd International Conference on Innovations in Information, Embedded, and Communication Systems (lCIIECS), 20I5.

12. Konstantin Shvachko, HairongKuang, Sanjay Radia, Robert Chansler, “The Hadoop Distributed File System”, in Proceedings of IEEE Symposium. Mass Storage Syst. Technol., 2010, pp. 1-10.

13. P.P.Ray, “A survey on Internet of Things architectures”, Journal of King Saud University - Computer and Information Sciences, Vol.30, Issue 3, pg: 291-319 (2018).

14. Pawan Kumar Mishra, G. P. Saroha, “A Study on Video Surveillance System for Object Detection and Tracking”, In Proceedings of the International Conference on Computing for Sustainable Global Development, ISBN 978-93-80544-20-5, IEEE, 2016.

15. Prof. A. M. Jagtap, Mr. VrushabhKangale, Mr. Kushal Unune, Mr. PrathmeshGosavi, “A Study of LBPH, Eigenface, Fisherface and Haar-like features for Face recognition using OpenCV”, International Conference on Intelligent Sustainable Systems (ICISS 2019) IEEE Xplore Part Number: CFP19M19- ART; ISBN: 978-1-5386-7799-5.

16. Rickin Patel, Vipul K. Dabhi, Harshadkumar B. Prajapati, “A survey on IoT based road traffic surveillance and accident detection system (A smart way to handle traffic and concerned problems)”, In Proceedings of the Innovations in Power and Advanced Computing Technologies (i-PACT), ISBN: 978- 1-5090-5683-5, IEEE, 2017.

17. S. Naga Jyothi and K. Vijaya Vardhan, “Design and implementation of real time security surveillance system using IoT”, In Proceedings of the International Conference on Communication and Electronics Systems (ICCES), ISBN: 978-1-5090-1067-7, IEEE, 2016.

18. SharminAkter, Rehana AfrozSima, Md. Sohid Ullah, Syed Akhter Hossain, “Smart Security Surveillance using IoT”, In Proceedings of the 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), ISBN: 978-1-5386-4693-9, IEEE, 2018.

19. Yingfeng Cai, Ze Liu, Hai Wang, Xiaoqiang Sun, “Saliency-Based Pedestrian Detection in Far Infrared Images”, Vol.5, 10.1109/ACCESS.2017.2695721.

20. Zhenfeng Shao, Jiajun Cai, and Zhongyuan Wang, “Smart Monitoring Cameras Driven Intelligent Processing to Big Surveillance Video Data”, IEEE Transactions on Big Data, Vol. 4, NO. 1, January- March 2018

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Published

30.06.2020

How to Cite

Rodda, A., S, S., Jaiganesh, M., & M, M. (2020). Suspect Detection System-An Architecture Based on Surveillance Visual Analytics using IoT. International Journal of Psychosocial Rehabilitation, 24(6), 18241-18253. https://doi.org/10.61841/wn3w8f36