Weapon Detection Using Artificial Intelligence and Deep Learning for S Application

Authors

  • P. Srinivas Babu Associate Professor, Department of Electronics and Communication Engineering, East West Institute of Technology, Bangalore, India
  • H. D. Sudarshan Student, Department of Electronics and Communication Engineering, East West Institute of Technology, Bangalore, India
  • B. L. Rakesh Gowda Student, Department of Electronics and Communication Engineering, East West Institute of Technology, Bangalore, India
  • P. Sujan Student, Department of Electronics and Communication Engineering, East West Institute of Technology, Bangalore, India
  • Suresh Mallappa Aldi Student, Department of Electronics and Communication Engineering, East West Institute of Technology, Bangalore, India

Keywords:

CCTV, CNN

Abstract

Now-a-days, many cases of crimes are report in public place, home using different types of weapons such as firearms, swords, cutters, etc. To monitor and minimize such types of crimes, CCTV camera is installed in public places. Generally, the video footages recorded through these cameras are monitored by security staff. Success and failure of detecting crime depends on the attention of operator. It is not always possible for a person to pay attention on all the video feeds on a single screen recorded through multiple video cameras. Nature and extent of crime depends on the types of weapon that is used. As a result, anomalies are influenced by the phenomena of interest. Object detection recognizes instances of several categories of objects using feature extraction and learning techniques or models The proposed implementation focuses on detecting and classifying guns accurately.

Downloads

Download data is not yet available.

Downloads

Published

23-07-2021

Issue

Section

Articles

How to Cite

[1]
P. S. Babu, H. D. Sudarshan, B. L. R. Gowda, P. Sujan, and S. M. Aldi, “Weapon Detection Using Artificial Intelligence and Deep Learning for S Application”, IJRESM, vol. 4, no. 7, pp. 279–281, Jul. 2021, Accessed: Nov. 09, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/1061