The aim of this thesis is to develop a low-cost, simple and unobtrusive surveillance system that uses video analysis for real-time face recognition and tracking and requires only a camera and a Raspberry Pi to operate. The aim of the thesis is to develop an advanced system that will be able to detect faces in videos and recognise them based on a pre-learned face recognition model. The system is designed for home security and notification of unauthorised entry.
Such a system has a number of important practical applications. Its main task is to improve security and surveillance in different environments. The system allows the recognition of persons appearing in a video feed, which is crucial for restricting unauthorised access to certain premises. This increases the level of security and reduces the risk of unauthorised access.
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