Threat Situation Determination System Through AWS-Based Behavior and Object Recognition


KIPS Transactions on Software and Data Engineering, Vol. 12, No. 4, pp. 189-198, Apr. 2023
https://doi.org/10.3745/KTSDE.2023.12.4.189,   PDF Download:
Keywords: Danger Situation Determination, AWS-based, object recognition, Behavior Recognition
Abstract

As crimes frequently occur on the street, the spread of CCTV is increasing. However, due to the shortcomings of passively operated CCTV, the need for intelligent CCTV is attracting attention. Due to the heavy system of such intelligent CCTV, high-performance devices are required, which has a problem in that it is expensive to replace the general CCTV. To solve this problem, an intelligent CCTV system that recognizes low-quality images and operates even on devices with low performance is required. Therefore, this paper proposes a Saying CCTV system that can detect threats in real time by using the AWS cloud platform to lighten the system and convert images into text. Based on the data extracted using YOLO v4 and OpenPose, it is implemented to determine the risk object, threat behavior, and threat situation, and calculate the risk using machine learning. Through this, the system can be operated anytime and anywhere as long as the network is connected, and the system can be used even with devices with minimal performance for video shooting and image upload. Furthermore, it is possible to quickly prevent crime by automating meaningful statistics on crime by analyzing the video and using the data stored as text.


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Cite this article
[IEEE Style]
Y. Kim, S. Jeong, S. Park, Y. Park, "Threat Situation Determination System Through AWS-Based Behavior and Object Recognition," KIPS Transactions on Software and Data Engineering, vol. 12, no. 4, pp. 189-198, 2023. DOI: https://doi.org/10.3745/KTSDE.2023.12.4.189.

[ACM Style]
Ye-Young Kim, Su-Hyun Jeong, So-Hyun Park, and Young-Ho Park. 2023. Threat Situation Determination System Through AWS-Based Behavior and Object Recognition. KIPS Transactions on Software and Data Engineering, 12, 4, (2023), 189-198. DOI: https://doi.org/10.3745/KTSDE.2023.12.4.189.