Design of an Intelligent Embedded Surveillance System Using Vision Transformers for Real-Time Threat Detection

Abstract

The rapid growth of intelligent surveillance technologies has significantly transformed modern security systems by enabling automated monitoring, threat identification, anomaly detection, and real-time decision-making across smart cities, industrial facilities, transportation networks, public infrastructures, and critical defense environments. Conventional surveillance systems primarily depend on human operators for continuous monitoring, making them susceptible to delayed responses, operator fatigue, limited situational awareness, and reduced detection accuracy in complex environments. Although Convolutional Neural Networks (CNNs) have demonstrated considerable success in visual recognition tasks, their limited capability to capture long-range spatial dependencies often restricts their performance in crowded and dynamically changing scenes. Vision Transformers (ViTs), which utilize self-attention mechanisms, have recently emerged as powerful computer vision architectures capable of learning global contextual relationships while achieving superior object recognition and scene understanding. This research proposes an Intelligent Embedded Surveillance System Using Vision Transformers for real-time threat detection by integrating embedded edge computing, high-resolution camera acquisition, image preprocessing, Vision Transformer-based feature extraction, intelligent threat classification, object tracking, and automated security alert generation. The proposed framework enables accurate identification of suspicious individuals, abandoned objects, weapons, unauthorized intrusions, and abnormal human activities while maintaining low inference latency and high computational efficiency on embedded hardware platforms. Experimental evaluation demonstrates significant improvements in detection accuracy, precision, recall, F1-score, inference speed, and energy efficiency compared with conventional CNNbased surveillance systems. The proposed intelligent embedded framework provides a scalable, lowpower, and reliable surveillance solution suitable for smart cities, airports, railway stations, industrial facilities, border security, military installations, public transportation systems, educational institutions, healthcare infrastructures, and future AI-enabled autonomous security ecosystems.