ІНТЕЛЕКТУАЛЬНИЙ АНАЛІЗ ОПТИЧНИХ ЗОБРАЖЕНЬ НА ОСНОВІ ГІБРИДНОГО ПІДХОДУ

The article considers an intelligent approach to real-time analysis of optical images based on a combination of face recognition methods using deep learning and classical computer vision algorithms for tracking them. A system with hybrid approach is proposed that integrates preliminary face recognition based on vector features (embeddings) generated by the FaceNet neural network and face tracking using the CSRT (Channel and Spatial Reliability Tracker) algorithm, which is part of the OpenCV library. The implemented system allows to recognise and automatically identify users in a video stream from a webcam, store new faces in the database, and effectively track identified faces over subsequent frames. The frame processing algorithm is implemented in a multi-threaded mode using queues and thread synchronisation mechanisms to ensure stable operation in real time. To recognise unknown persons, a unique ID is automatically created and their features are added to the common database of emblems. Particular attention is paid to assessing the spatial overlap of detection zones to avoid duplication of trackers when several people are present in the frame at the same time. In addition, the system is implemented as a web service based on Flask, which provides convenient integration with other software modules and the possibility of remote monitoring via a web interface. The proposed hybrid approach combines the accuracy of modern deep learning models with the flexibility of classical tracking algorithms, making the system suitable for use in security systems, smart offices, educational environments, and other areas where accurate face identification in dynamic environments is important. In summary, this paper demonstrates the practical implementation of an intelligent image analysis system that can be adapted to various use cases, including video surveillance, access control, and crowd management systems, as well as research and educational projects.