Subhani Shaik, V Kakulapati*, Saadiq, Ontela Sanjay and Krishna Reddy

Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana, India

*Corresponding Author: V Kakulapati, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana, India.

Received: March 25, 2023; Published: April 12, 2023

Abstract

Big data applications are consuming the bulk of the space in industry and research. CCTV camera video streams are just as important as social media data, sensor data, agricultural data, medical data, and data produced from space research when it comes to big data. Surveillance videos provide a substantial contribution to unstructured big data. All places where security is a concern have CCTV cameras installed. It indicates that manual surveillance is inconvenient and time-consuming. Depending on the scenario, security may be defined in a variety of ways, such as detecting theft, detecting violence, estimating the risk of an explosion, and so on. The phrase "security" in crowded public places applies to almost any uncommon incident. In addition to assessing whether the captured movements are odd or suspicious, it demands a workforce and constant attention. Much of the research in the literature review suggested implementing surveillance using hardware and software tools that take video as input and require massive datasets. As it includes group activities, detecting violence among them is tough. Due to various real-world constraints, detecting anomalous or aberrant behaviour in a crowd video scene is extremely challenging. This work begins with item recognition in a crowded area. The main goal of this application is to identify weapons in the surrounding area, such as guns, knives, and fire, and to notify management of the potential threat by sending a screenshot to the user interface. This could be a good way for security and law enforcement staff to find out about the weapon in the surveillance.

Keywords: Yolo Version-4 Algorithm; Surveillance Videos; CCTV Camera; Threat Detection; Sensors; Violence; Risk; Hardware

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Citation

Citation: V Kakulapati., et al. “Real-Time Threat Detection Using the Yolo Version-4 Algorithm". Acta Scientific Computer Sciences 5.5 (2023): 50-55.

Copyright

Copyright: © 2023 V Kakulapati., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.