Automated Real Time Detection of Suspicious Appearances Using Deep Learning

  • Melek Tursun National Defense University
  • Ömer Çetin National Defense University
Keywords: Detection of Suspicious Behaviors, MTCNN, Deep Learning, Image Processing

Abstract

Security camera systems especially in public areas such as airports, courthouses or sports facilities etc. are used to find fugitive persons or detect suspicious behaviors manually under the monitoring of an operator. In hallway-like sections in public facilities, repeated appearances of an unknown ordinary person in a short span of time can be defined as suspicious behavior. However, the fact that multiple cameras are monitored by a single operator makes it harder to detect suspicious behaviors especially in crowded fields. Therefore, support decision systems are required to support operator. If individuals are detected on images automatically and their appearances on the camera are recorded on a database by giving them a temporary identity, suspicious behaviors can be reported to an operator as a support decision system. For this reason, two different methods are used together as a hybrid solution in the study; a MTCNN based facial detection is used on the real time security camera images that currently provide face images, and an identification method, created with facial landmarks produced with a deep learning algorithm that was trained with res-net, was used on the obtained person’s face images. It has been presented that suspicious behaviors can be detected by interpreting the temporary identity information that was obtained. The success of the application was experimentally tested, and the causes of success and failures in the results were discussed.

Author Biography

Ömer Çetin, National Defense University

Ömer Cetin is currently an assistant professor at the Computer Engineering Department of National Defense University (NDU), Turkey. He received his B.Sc.  degree in Computer Engineering from Turkish Air Force Academy, Istanbul, in 2003. He received his M.Sc.  degree in Software Engineering from Aeronautics and Space Technologies Institute (ASTIN), Istanbul, Turkey, in   2008. Asst. Prof. Dr. Ömer ÇETİN received his Ph.D. degree in Computer Engineering Program in Department of Computer Engineering of ASTIN in 2015. He is currently researching related with cyber security, deep learning, and autonomous systems.

Published
2021-01-21
How to Cite
[1]
M. Tursun and Ömer Çetin, “Automated Real Time Detection of Suspicious Appearances Using Deep Learning”, JAST, vol. 14, no. 1, pp. 71-78, Jan. 2021.
Section
Articles