"EXPLORATION OF REAL-TIME EYE DROWSINESS DETECTION THROUGH DIVERSE COMPUTER VISION MODELS: AN INVESTIGATIVE STUDY" (European Journal of Emerging Technology and Discoveries, 2024)
This research investigates the real-time detection of drowsiness through an in-depth exploration of diverse computer vision models. Key words such as "drowsiness detection," "computer vision," "eye behavior," and "real-time analysis" underscore the focal points of this study. Leveraging advancements in computer vision, the study scrutinizes various models to discern their efficacy in promptly identifying signs of drowsiness based on subtle eye movements. Employing a systematic methodology, the research rigorously evaluates the accuracy, efficiency, and real-time applicability of these models. By conducting a comparative analysis, this study seeks to uncover the most robust and reliable methods for detecting drowsiness, crucial for preemptive interventions in scenarios prone to fatigue-related risks. The findings illuminate the potential of these computer vision models in enhancing safety across diverse domains, particularly in transportation and occupational settings. A nuanced understanding of eye behavior and its correlation with drowsiness provides a foundation for the development of sophisticated real-time drowsiness detection systems. These systems hold promise in mitigating risks associated with fatigue-induced impairments, thereby fostering safer environments, and improving operational efficiency. This research sets the stage for future advancements in proactive safety measures through the integration of cutting-edge computer vision technologies.