Efficiently tracking and recognition of human faces in real-time video stream with high accuracy and performance

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The system achieves high accuracy in tracking human faces in real-time video streams, even under challenging conditions.
Key evidence shows integration of YOLOv5 with SORT leads to improved performance metrics and reduced latency.
Deep learning algorithms combined with GPU acceleration provide efficient processing for real-world applications.
Application potential in surveillance and security highlights the system's real-time monitoring capabilities in crowded environments.

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Research demonstrates enhanced accuracy and efficiency in tracking human faces, suggesting better tools for computer vision applications.

Khan et al. (2025) studied this question.

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