Optimized Deep Learning System for Criminal Activity Detection using Facial Recognition, Video Surveillance, and Weapon Detection

Abstract

The evolving field of deep learning has automated intelligent surveillance and criminal activity detection systems. This paper provides refined optimized deep learning architecture systems aimed at improving crime detection using facial recognition, real-time video surveillance systems, and weapon detection. These systems utilize the latest neural network structures, chiefly CNNs, RNNs, and hybrid deep learning models, to derive accuracy, real-time analysis, and scalability.   The automated facial recognition systems track users' behaviors, registers recognized users, recognizes suspicious behaviors, and detects weapons at video feed frames. Using spatial-temporal analysis helps detect and reduce response delays in public areas, transport systems, and other sensitive places.   Important issues such as dataset deficiencies, real-time computing limits, aggressive attacks, and ethical concerns on surveillance AI are given attention. Transfer learning, model compression, edge computing, and AI transparency are some solutions put forward. The paper addresses these challenges and emphasizes the need for diverse, representative training datasets to strengthen system dependability and equity. Future research and development may benefit from the discussion highlighting transformer-based architectures, multimodal fusion, and other emerging trends which incorporate reinforcement learning. This review furthers the state of the art by integrating advanced technology development and documenting noted advancements, turning them into actionable frameworks by formulating real-world deployment strategies.  The primary contribution of this paper is to assist researchers, developers, and even policymakers comprehend in-depth, the potential deep learning methods offer in the design of surveillance systems for criminal activity detection that are safe, smart, and ethically responsible.