A Conceptual Framework for Multi-Sensor Human Detection in Intelligent Perimeter Security Systems

In an era increasingly defined by smart technologies and real-time data-driven systems, securing perimeters against unauthorized intrusions has become paramount. This research proposes a low-cost, intelligent, and power-efficient perimeter protection system that integrates multi-modal sensors—namely the HLK-LD2410B millimetre-wave radar and the Adafruit AMG8833 thermal infrared sensor—with image capture via the ESP32-CAM and centralized control through the ESP8266 microcontroller. The core novelty lies in a dual-stage machine learning pipeline that fuses thermal and visual data to accurately detect human presence while minimizing false positives. The system operates on a hierarchical decision framework, initiating with radar motion detection, followed by conditional thermal sensing, and concluding with CNN-based image validation. Upon confirmed detection, alerts are issued both locally via a buzzer and remotely through Wi-Fi-enabled notifications. Simulation and field tests demonstrate over 97% accuracy, sub-300 ms latency, and high energy efficiency, validating the system's suitability for smart surveillance, elderly monitoring, and adaptive home automation applications.