A survey on drowsiness detection system with advanced face tracking
1 Assistant Professor, Department of CSE (Artificial Intelligence & Machine Learning), ACE Engineering College, Hyderabad, Telangana, India.
2 IV B. Tech students Department of CSE (Artificial Intelligence & Machine Learning), ACE Engineering College, Hyderabad, Telangana, India.
Review Article
World Journal of Advanced Research and Reviews, 2024, 21(03), 1748–1753
Article DOI: 10.30574/wjarr.2024.21.3.0809
Publication history:
Received on 08 February 2024; revised on 16 March 2024; accepted on 19 March 2024
Abstract:
To address the increasing dangers associated with driver and worker fatigue, this project introduces an advanced Drowsiness Detection System featuring state-of-the-art face-tracking capabilities. The pressing need for fatigue detection is evident in the alarming figures of 800 annual fatalities and 50,000 injuries resulting from drowsy driving incidents. This research expands the application of the technology to industrial workplaces, where the consequences of drowsiness are equally severe. Our comprehensive approach involves real-time monitoring of facial features, with a focus on eye movements and eyelid patterns. This system goes beyond traditional boundaries, covering drivers and industrial workers operating heavy machinery. By incorporating facial landmarks and introducing the innovative Eyes Aspect Ratio parameter, our technology offers a precise assessment of weariness in individuals within the current frame. This approach enhances safety measures in smart transportation systems and industrial settings. Integrating facial landmarks allows for a nuanced understanding of fatigue, recognizing subtle changes in facial expressions and movements indicative of drowsiness. The Eyes Aspect Ratio parameter, a novel addition, improves weariness assessment precision by considering factors such as eye closure duration, blink frequency, and gaze direction. These outcomes signify a significant contribution to road safety and broader workplace security. By mitigating inherent risks associated with drowsiness among industrial workers, our technology aims to reduce accidents, prevent injuries, and save lives across various occupational settings. The potential impact extends beyond individuals, influencing organizational safety protocols and contributing to the overall well-being of workers in high-risk environments.
Keywords:
Drowsiness Detection System; Face tracking capabilities; Real-time monitoring; Eye movements; Facial landmarks; Eyes Aspect Ratio parameter.
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0