Propounding First Artificial Intelligence Approach for Predicting Robbery Behavior Potential in an Indoor Security Camera

S Sreenivasulu; Gonugunta venkata Bharathkumar

Author(s)

S Sreenivasulu Dept of Computer Science and Engineering, Prakasam Engineering College,,India

Gonugunta venkata Bharathkumar Dept of Computer Science and Engineering, Prakasam Engineering College, India,

Abstract

For video surveillance systems to avoid incidents and safeguard assets, crime prediction is necessary. In this regard, our paper suggests the first artificial intelligence method for predicting and detecting Robbery Behavior Potential (RBP) in an interior camera. Three detection modules-head cover, crowd, and loitering detection modules-are the foundation of our approach, which enables prompt response and deters robberies. The YOLOV5 model is retrained using the manually annotated dataset we collected to create the first two modules. Furthermore, using the Deep SORT algorithm, we provide a new definition for the loitering detection module. After converting expert information into rules, a fuzzy inference system makes a final determination about the likelihood of robbery. The robber's various style, the security camera's variable viewpoint, and the poor quality of the video photos make this tedious. We successfully completed our experiment using actual video surveillance footage, achieving an F1-score of 0.537. Therefore, we design a threshold value for RBP to assess video pictures as a robbery detection issue in order to do an experimental comparison with the other relevant research. Assuming this, the experimental findings clearly reflect an F1-score of 0.607, indicating that the suggested approach performs much better in identifying the heist than the robbery detection methods. We firmly think that by anticipating and averting robbery incidents, the implementation of the suggested strategy might reduce the harm caused by robberies in a security camera control center. However, the human operator's situational awareness improves and additional cameras may be controlled

Publication Details

Page(s) 88-98

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 5, Year: 2024

Request the full text from PASTIC

Keywords

fuzzy inference machine, deep learning method, Surveillance videos, low resolution, RBP prediction

References

Ferguson A. G. .2012 .'Predictive policing and reasonable suspicion. , 62(2) : 259.

Beck C.,McCue C. .2009 .'Predictive policing: What can we learn from Wal-Mart and Amazon about fighting crime in a recession?'' Police Chief. , 76(11) : 18.

Bowers K. J.,Johnson S. D. .2004 .'Who commits near repeats? A test of the boost explanation,'' Western Criminol. Rev., 5(3) : 12-24.

.2021 .SPD_CRIME_REPORT_FINAL. , : .

.2019 .gov/ crime-in-the-u.s/2019/crime-in-the-u. , : .

Fawei B.,Pan J. Z.,Kollingbaum M.,Wyner A. Z. .2019 .'A semi-automated ontology construction for legal question answering. '' New Gener.Comput., 37(4) : 453-478.

Thompson R.,Nottingham U.K. .2014 .'Understanding theft from the person and robbery of personal property victimisation trends in England. Tech. Rep, : .

Cook P. J. .1987 .. , 78(2) : 357-376.

J. D. McCluskey .2013 .'A comparison of Robbers' use of physical coercion in commercial and street robberies,'' Crime Delinquency. , 59(3) : 419-442.