ARMS DETECTION SYSTEM USING WEBCAM

ABSTRACT This study investigates the integration of arm detection technology into home security systems as a means to enhance threat identification and reduce false alarms. Traditional motion detection systems, while widely adopted, often suffer from inaccuracies due to environmental factors and benign movements. By leveraging advancements in artificial intelligence (AI), computer vision, and deep learning particularly convolutional neural networks (CNNs) arm detection offers a more precise approach to identifying suspicious human behavior. The research evaluates the effectiveness of arm detection under various environmental conditions, its compatibility with existing security infrastructure, and addresses challenges related to privacy and computational efficiency. Using a structured methodology based on the System Development Life Cycle (SDLC), feasibility analysis, and experimental data collection, the study provides practical recommendations for deploying arm detection in residential settings. The findings contribute to the advancement of intelligent surveillance technologies and offer actionable insights for homeowners and security providers seeking more reliable and responsive security solutions.