Secure Vision: Real-Time AI Surveillance Framework For Tactical Threat Recognition

Abstract

Deep learning is moving toward automating intelligent surveillance and criminal activity detection systems. In this paper, optimized deep learning architectures have been proposed that have been optimized to enhance the detection of crime by facial recognition, real-time video surveillance system, and weapon detection. These systems achieve accuracy, real-time analysis, and scalability through the use of the latest neural network structures, primarily CNNs, RNNs, and hybrid deep learning models. The automated facial recognition systems can track users’ behaviors, register recognized users, recognize suspicious behaviors and detect weapons at video feed frames. Through spatial-temporal analysis, Aides can identify and mitigate response delays in public areas, transport systems and other sensitive locations. Important issues including dataset deficiencies, limits of real-time computing, aggressive attacks and ethical concerns on surveillance AI are addressed. Some proposed solutions include transfer learning, model compression, edge computing, and AI transparency. The paper deals with these challenges and discusses the importance of diverse and representative training datasets for enhancing reliability and fairness of the system. The discussion emphasizes the use of transformer-based architectures, multimodal fusion, and other emerging trends involving reinforcement learning that may benefit future research and development. This review pushes the state of the art further by including advanced technology development and noting advancements, making them actionable frameworks by formulating real-world deployment strategies.  The main contribution of this paper is to help researchers, developers and even policy makers to have an in-depth understanding of the potential that deep learning methods offer in the design of safe, smart and ethically responsible surveillance systems for criminal activity detection.