Mathematical Models Using Artificial Intelligence for Surveillance Systems

5Enhancing Surveillance Systems through Mathematical Models and Artificial Intelligence: An Image Processing Approach

Tarun Kumar Vashishth*, Vikas Sharma, Bhupendra Kumar, Kewal Krishan Sharma, Sachin Chaudhary and Rajneesh Panwar

School of Computer Science and Applications, IIMT University, Meerut U.P., India

Abstract

This study presents an extensive exploration of the integration of mathematical models and artificial intelligence (AI) techniques in surveillance systems based on image processing. The study delves into various mathematical modeling approaches and their fusion with AI techniques to address key challenges in object detection, recognition, behavior analysis, and video analytics within surveillance systems. The research investigates the utilization of convolutional neural networks (CNNs) for object detection and recognition tasks, highlighting the significant advancements achieved in accuracy and efficiency through these models. Additionally, the study explores the integration of recurrent neural networks (RNNs) and other deep learning architectures for behavior analysis, empowering surveillance systems to detect and predict suspicious activities or anomalous behavior. The integration of mathematical models and AI techniques in surveillance systems is a rapidly evolving field with promising applications across various domains. By combining mathematical models with AI algorithms, surveillance systems gain the ability to process and interpret large volumes of visual ...