Summary of Real-time Drowsiness Detection Using Eye Aspect Ratio and Facial Landmark Detection, by Varun Shiva Krishna Rupani et al.
Real-Time Drowsiness Detection Using Eye Aspect Ratio and Facial Landmark Detection
by Varun Shiva Krishna Rupani, Velpooru Venkata Sai Thushar, Kondadi Tejith
First submitted to arxiv on: 11 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a real-time system for detecting drowsiness using Eye Aspect Ratio (EAR) and facial landmark detection techniques. The system utilizes the Dlibs pre-trained shape predictor model to accurately detect 68 facial landmarks, which are used to compute the EAR. By establishing an EAR threshold, the system identifies when eyes are closed, indicating potential drowsiness. The process involves capturing a live video stream, detecting faces in each frame, extracting eye landmarks, and calculating the EAR to assess alertness. Experimental results show that the system reliably detects drowsiness with high accuracy while maintaining low computational demands. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how to make a machine that can tell when someone is sleepy. It uses special computer vision techniques to look at people’s faces and see if their eyes are closed. This is important because it could help keep drivers awake on the road or workers safe in factories. The system works by using a camera to take pictures of people’s faces, finding the important parts like the eyes, and then comparing them to see if they’re closed. The results show that this system can work really well and might be used in the future to make roads and workplaces safer. |
