![](/image/LogoVKU.png)
Please use this identifier to cite or link to this item:
https://elib.vku.udn.vn/handle/123456789/3477
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Tran, Thanh | - |
dc.contributor.author | Nguyen, Van Chuc | - |
dc.date.accessioned | 2024-01-23T03:30:17Z | - |
dc.date.available | 2024-01-23T03:30:17Z | - |
dc.date.issued | 2024-01 | - |
dc.identifier.uri | http://elib.vku.udn.vn/handle/123456789/3477 | - |
dc.description | Graduation thesis of Computer Engineering Technology; 2019 - 2024. | vi_VN |
dc.description.abstract | In this project, we aim to find real-time responsive algorithms, optimal and simple solutions that provide high accuracy and meet practical needs. We simulate simple image processing problems, static image recognition, and direct recognition through the camera using Python programming. We also develop accurate eye state tracking in realtime on the Raspberry Pi 4 Kit. Face detection is performed using the AdaBoost classifier method based on Haar-like features. Eye recognition is achieved by marking the facial features using the Facial Landmarks algorithm, and then calculating the Euclidean distance between the two eye folds to detect the eye state and identify drowsiness. | vi_VN |
dc.language.iso | en | vi_VN |
dc.publisher | Vietnam-Korea University of Information and Communication Technology | vi_VN |
dc.relation.ispartofseries | KLTN;19CE007 | - |
dc.subject | Detect Driver Drowsiness | vi_VN |
dc.subject | Raspberry PI | vi_VN |
dc.title | Image Processing Application to Detect Driver Drowsiness using Raspberry PI kit | vi_VN |
dc.title.alternative | Ứng dụng xử lý ảnh phát hiện tài xế ngủ gật sử dụng kit Raspberry | vi_VN |
dc.type | Thesis | vi_VN |
Appears in Collections: | ĐH-Ngành Công nghệ kỹ thuật máy tính (Computer Engineering) |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.