Abstract:
"Contemporary surveillance systems exhibit fundamental limitations in threat detection, relying predominantly on isolated weapon identification or behavioral analysis rather than integrated contextual assessment. Current automated approaches achieve merely 70-85% accuracy with substantial false positive rates, primarily due to the absence of spatial-temporal contextual intelligence and human-weapon proximity validation. This research addresses the critical accuracy gap in existing surveillance technology through the development of a novel Spatiotemporal Contextual Threat Inference (SCTI) algorithm.
An innovative deep learning framework integrating YOLOv5-based weapon detection with 3D Convolutional Neural Networks for spatiotemporal action recognition was developed. The core methodological innovation lies in the SCTI algorithm, which employs human-weapon spatial relationship validation coupled with temporal behavioral consistency analysis across 3-5 second windows. The framework implements hybrid fusion strategies, combining early-stage feature integration with late-stage decision fusion. Enhanced datasets incorporating UCF101, HMDB51, and custom surveillance recordings underwent rigorous augmentation and transfer learning protocols to optimize model robustness.
The SCTI-enhanced prototype achieves 88.4% overall accuracy with early fusion methodology, representing significant advancement over baseline approaches. Weapon detection attains 87% F1-score through YOLOv5 optimization, while spatiotemporal action recognition demonstrates 91% accuracy for critical suspicious behaviors. The system maintains real-time processing at 25 frames per second with 39 millisecond latency using conventional GPU hardware. User validation confirms 84% contextual relevance of generated alerts, indicating a 60-70% reduction in false positives compared to conventional systems
Subject Descriptors:
• Computing methodologies → Computer vision → Object detection
• Computing methodologies → Machine learning → Machine learning approaches → Neural networks
• Applied computing → Law, social and behavioral sciences → Surveillance systems
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