Edge-computing-based tactile neuromorphic sentry system for intelligent parking surveillance

This paper presents a novel neuromorphic tactile sensing system inspired by human skin–neuron–cortex pathways, developed to address the energy and privacy challenges in modern vehicular surveillance. The proposed system integrates a heterojunction piezoelectric transistor as a tactile sensory node based on stacked up structure of ZnO/NiO/ZnO semiconductors with a neuromorphic matrix computing chip designed for on-chip classification of tactile events. The sensor exhibits an enhanced signal-to-noise ratio and high sensitivity via internal current and voltage amplification, while the neuromorphic circuit implements core neural network functions with parallel low-power processing. By extracting frequency-domain features to classify physical events such as impact, scraping, rainfall, and hail in parking scenarios, our system demonstrates real-time recognition with 93.1% accuracy on hardware and 99.2% in software, all while significantly reducing power consumption compared to conventional vision-based surveillance systems and avoiding visual privacy concerns. This work offers a promising solution for energy-efficient, event-driven smart surveillance in edge environments.