Published 31 Dec 2019 •  vol 12  •  no 2  • 



P. Aleemulla Khan, Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam-530049, India
N. Thirupathi Rao, Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam-530049, India
Debnath Bhattacharyya, Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam-530049, India



To provide effective security in crowded or public areas in today’s world is a big challenge for us. One of the major challenges is to detect or monitor potential threats such as explosive items or bombs (Abandoned luggage items).In this paper we propose an approach for automatic detection of abandoned luggage and alerting the security alliances ,We use deep learning to train the system with a set of images, these images were given to the trained system which is going to visualize the objects in the image and calculate the distance between objects if the object is person and baggage or only baggage. If the distance is greater than a threshold distance limit then the system is going to raise an alarm for the security alliances.



Explosive items, malicious items, Deep Learning, Security alliances



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Khan, P. A., Rao, N. T., & Bhattacharyya, D. (2019). Malicious Items Detection at Public Places using Deep Learning Methods. International Journal of Grid and Distributed Computing (IJGDC), ISSN: 2005-4262 (Print); 2207-6379 (Online), NADIA, 12(2), 19-30. doi: 10.33832/ijgdc.2019.12.2.02.

Khan, P. Aleemulla, et al. “Malicious Items Detection at Public Places using Deep Learning Methods.” International Journal of Grid and Distributed Computing (IJGDC), ISSN: 2005-4262 (Print); 2207-6379 (Online), NADIA, vol. 12, no. 2, 2019, pp. 19-30. IJGDC,

[1] P. Aleemulla Khan, N. Thirupathi Rao, and D. Bhattacharyya, "Malicious Items Detection at Public Places using Deep Learning Methods." International Journal of Grid and Distributed Computing (IJGDC), ISSN: 2005-4262 (Print); 2207-6379 (Online), NADIA, vol. 12, no. 2, pp. 19-30, Dec 2019.