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Pixel Guardians on Enhancing Public Safety Through Image Processing in Suspicious Activity Recognition
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Author(s): R. Radhika (Bharath Institute of Higher Education and Research, India)and A. Muthukumaravel (Bharath Institute of Higher Education and Research, India)
Copyright: 2026
Pages: 24
Source title:
Pioneering AI and Data Technologies for Next-Gen Security, IoT, and Smart Ecosystems
Source Author(s)/Editor(s): Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand), Karthikeyan Chinnusamy (Veritas, USA), Joseph Jeganathan (University of Bahrain, Bahrain), Ahmed J. Obaid (University of Kufa, Iraq)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3373-4672-4.ch004
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Abstract
This chapter focuses on improving the existing monitoring system's ability to detect and prevent disruptive behaviours, which is critical given the growing public safety concerns. Recognizing patterns connected with potential threats, inappropriate behavior, and security issues can be challenging in many scenarios. The proposed approach of integrating attention mechanisms into image processing is suggested as a solution to detect suspicious activities. By employing this novel methodology, the model is optimized, and the design language is simplified, thereby ensuring enhanced precision in surveillance. The principal aim of the framework is to augment the model's sensitivity to significant temporal and spatial variables. A Convolutional Neural Network powers it with attention elements that are strategically positioned. To improve activity detection and public safety, the suggested approach places a higher value on refining classification than employing complex language.
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