Human Anomaly Detection using Deep Learning

Authors

  • Lambu Yashaswi Student, Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India
  • Sreenivas Mekala Associate Professor, Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India
  • Maktala Durga Prasad Student, Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India

DOI:

https://doi.org/10.51983/ajcst-2023.12.1.3630

Keywords:

Human Anomaly Detection, Deep Learning, LRCN, LSTM, CNN

Abstract

Human Anomaly Detection can be used in order to identify thefts, terrorist attacks, fighting, and fires in susceptible areas including banks, parking areas, hospitals, shopping malls, universities, colleges, schools, borders, airports, bus and railway stations, etc. Video surveillance can be used in crowded areas to identify anomalies and analyse human behaviour to detect theft and vandalism. It will also help to prevent inappropriate behaviour such as fighting among humans by monitoring the perimeter of the location, for the safety of people. It can be used to monitor the suspicious activity of humans in crowded places.

References

P. Bhagya Divya, S. Shalini, R. Deepa, and Baddeli Sravya Reddy, "Inspection of suspicious human activity in the crowdsourced areas captured in surveillance cameras," International Research Journal of Engineering and Technology (IRJET), Vol. 04, No. 12, December 2017. [Online]. Available: https://qu.edu.iq/journalcm/index.php/journalcm/article/view/858

Prof. Jitendra Musale, Miss. Akshata Gavhane, Mr. Liyakat Shaikh, Miss. Pournima Hagwane, and Miss. Snehalata Tadge, "Suspicious Movement Detection and Tracking of Human Behavior and Object with Fire Detection using Closed Circuit TV (CCTV) cameras," International Journal for Research in Applied Science and Engineering Technology (IJRASET), Vol. 5, No. 12, pp. 2013-2018.

R. Dinesh Jackson Samuel, E. Fenil, Gunasekaran Manogaran, G. N. Vivekananda, T. Thanjaivadivel, S. Jeeva, and A. Ahilan, "Real-time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM," The International Journal of Computer and Telecommunications Networking, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S1389128618308521.

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Published

03-06-2023

How to Cite

Yashaswi, L., Mekala, S., & Prasad, M. D. (2023). Human Anomaly Detection using Deep Learning. Asian Journal of Computer Science and Technology, 12(1), 35–40. https://doi.org/10.51983/ajcst-2023.12.1.3630

Issue

Vol. 12 No. 1 (2023): January-June 2023

Section

Articles

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Copyright (c) 2023 The Research Publication

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