<![CDATA[Ensuring laboratory security is a critical consideration within campus environments to effectively prevent theft and suspicious activities. Traditional CCTV systems predominantly rely on manual monitoring, resulting in delayed responses to incidents. This research seeks to develop and implement an Artificial Intelligence (AI)-based laboratory security system, integrating three primary models: YOLOv5 for human object detection, Face Recognition for individual identification, and Media Pipe Pose for real-time analysis of suspicious movements. The system is designed as a Flask-based monitoring website, which displays activity logs, detected individual identities, and automated notifications based on image processing results on a Raspberry Pi connected to CCTV cameras. The research methodology employs an applied experimental approach, encompassing stages such as system design, face dataset collection, data encoding utilizing the Face Recognition Library, and performance evaluation under two lighting conditions (bright and dark) and three distance variations. The test results indicate that the Face Recognition method operates optimally at a distance of 2 meters in bright lighting conditions, achieving an accuracy of up to 92%. However, its performance declines at distances exceeding 3 meters and under low-light conditions. Conversely, MediaPipe Pose exhibits high stability, with an average accuracy of 94% in bright conditions and 89% in dark conditions, and is capable of transmitting real-time notifications for activities such as lifting objects or placing hands into pockets. The AI-based laboratory security system developed has demonstrated effectiveness, adaptability, and responsiveness in the automatic detection of identities and suspicious activities. The integration of YOLO v5, Face Recognition, and MediaPipe Pose models offers an intelligent and efficient security solution that facilitates the implementation of the Smart Campus concept within educational environments.]]>
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