SMARTVISION-AI: A UNIFIED DEEP LEARNING ARCHITECTURE FOR FACE RECOGNITION AND WEAPON DETECTION IN CCTV VIDEO STREAMS

Abstract

Intelligent surveillance systems need very dynamic visual intelligence that will recognise individuals and mark the presence of possible threats in real-time video surveillance. To overcome this requirement, a single deep learning system called SmartVision-AI is proposed, which combines face recognition and weapon identification in one feature-based architecture. The approach uses a dual-branch convolutional encoder, attention-based feature fusion, and multi-task learning, which is optimized towards low-latency CCTV systems. Tests on mixed-face and weapon image data sets show that the architecture can deliver face recognition accuracy of 96.8%, multi-class weapon detection accuracy of 94.7%, a false-positive rate drop to 21%, a precision increase of up to 19%, and a processing time of only 38 ms/frame, which can be effectively deployed in near real-time. Further processing indicates that there is an increase in temporal stability by 32% and a reduction in the use of GPU memory by 27% in comparison with individual task-specific models. The findings attest to the fact that SmartVision-AI provides a powerful, effective, and scalable intelligent threat-aware video surveillance system.