Localization of Strangeness for Real Time Video in Crowd Activity Using Optical Flow and Entropy

Authors

  • Ali Abid Hussan Altalbi University of Technology, Baghdad, Iraq https://orcid.org/0009-0002-4364-364X
  • Shaimaa Hameed Shaker University of Technology, Baghdad, Iraq
  • Akbas Ezaldeen Ali

DOI:

https://doi.org/10.3991/ijoe.v19i07.38869

Keywords:

abnormal event detection, RGB frames, gray frames, optical flow, farneback method, Entropy, Activity Map.

Abstract


Anomaly detection, which is also referred to as novelty detection or outlier detection, is process of identifying unusual occurrences, observations, or events which considerably differ from the bulk of data and do not fit a predetermined definition of typical behavior. Medicine, cybersecurity, statistics, machine vision, law enforcement, neurology, and financial fraud are just a handful of the industries where anomaly detection is used. In the presented study, an online tool is utilized to identify crowd distortions, which could be brought on by panic. An activity map is produced with the use of numerous frames to show the continuity regarding the flow over time following the global optical flow has been calculated in the quickest time and with the highest precision possible utilizing the Farneback approach to calculate the magnitudes. Utilizing a specific threshold, the oddity in the video will be picked up by the activity map's generation of an entropy. The results indicate that the maximum entropy level for indoor video is <0.16 and the maximum entropy level for outdoor video is >0.45. A threshold of 0.04 is used to determine whether a frame is abnormal or normal.

Cover iJOE Vol. 19 No. 07 (2023)

Downloads

Published

2023-06-13

How to Cite

Abid Hussan Altalbi, A., Hameed Shaker, S. ., & Ezaldeen Ali , A. (2023). Localization of Strangeness for Real Time Video in Crowd Activity Using Optical Flow and Entropy. International Journal of Online and Biomedical Engineering (iJOE), 19(07), pp. 52–68. https://doi.org/10.3991/ijoe.v19i07.38869

Issue

Vol. 19 No. 07 (2023)

Section

Papers

License

Copyright (c) 2023 Ali Abid Hussan

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution Licence (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
By submitting an article the author grants to this journal the non-exclusive right to publish it. The author retains the copyright and the publishing rights for his article without any restrictions.