AUTOMATED BANDITRY DETECTION AND ALARM SYSTEM USING REAL-TIME CAMERA SURVEILLANCE AND AI-BASED THREAT RECOGNITION
Abstract
ABSTRACT
This project presents the design and implementation of an Automated Banditry Detection and Alarm System using real-time camera surveillance and AI-based threat recognition. The system employs the ESP32-CAM module to capture live images which are processed and analyzed using a machine learning model. Principal Component Analysis (PCA) is used for feature extraction, while a Support Vector Machine (SVM) classifier is trained to detect the presence or absence of a threat. Upon detecting a threat, the system activates a buzzer, and displays a warning message on an LCD. The system is powered by a 9V battery and is designed to operate efficiently in remote or security-sensitive areas. This project demonstrates the potential of combining embedded systems with artificial intelligence to enhance public safety through automated, low-cost surveillance solutions.
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