Implementation of Real-time Focus Tracking in Video Streams through Advanced Computational Methods and Image Analysis
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Date
2024
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Faculty of Engineering and Natural Science
Abstract
This research presents the development and implementation of a real-time focus tracking system in video streams, utilizing advanced computational methods and image analysis techniques. The core of the system is based on the L2CS-Net model, a convolutional neural network known for its efficacy in gaze estimation. The objective of this research was to use it for real-world applications, particularly in settings where user engagement and attention monitoring are crucial, such as e-learning and virtual meetings. To address the challenges of variable video quality and user behavior typically encountered outside laboratory conditions, the L2CS-Net model was refined using a comprehensive dataset, the MPIIGaze, with specific focus on preprocessing techniques that enhance training efficiency and model accuracy. The practical deployment of the model was achieved through a web-based interface, developed to provide real-time feedback on user focus. This interface was hosted locally on a Flask API, ensuring ease of deployment and making changes. A key feature of the system is an alert mechanism that notifies users when the detected gaze deviates beyond set thresholds, indicating potential lapses in attention. Experimental results demonstrate that the enhanced L2CS-Net model achieves high accuracy in gaze prediction. Furthermore, the web application’s real-time processing capabilities and the effectiveness of the alert system were validated under operational conditions. Future works can explore the integration of additional sensor data and improvements in alert accuracy, aiming to further enhance user engagement and monitoring in virtual environments.
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Seitkaliyeva G / Implementation of Real-time Focus Tracking in Video Streams through Advanced Computational Methods and Image Analysis / 2024 / Computer Science - 7M06102