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생체측정 플랫폼 기반 실시간 차량용 운전자 모니터링 시스템
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 김영길 | - |
| dc.contributor.author | 박화범 | - |
| dc.date.accessioned | 2022-11-29T03:01:07Z | - |
| dc.date.available | 2022-11-29T03:01:07Z | - |
| dc.date.issued | 2020-02 | - |
| dc.identifier.other | 29587 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/20770 | - |
| dc.description | 학위논문(박사)--아주대학교 일반대학원 :의용공학과정,2020. 2 | - |
| dc.description.tableofcontents | ABSTRACT (국문요약) 1. Introduction 1 1.1. Study background 1 1.2. Study purpose 6 1.3. Composition of thesis 7 2. Bio-signal emotion recognition related theory 9 2.1. Facial expression bio-signal 9 2.1.1 Physical facial feature 9 2.1.2 Type of facial expression 14 2.2. Voice bio-signal 16 2.2.1 Generation of voice 16 2.2.2 Acoustic emotional information 17 2.3. EEG bio-signal 18 2.3.1 How to measure EEG 18 2.3.2 EEG emotion recognition 19 3. DMS(Driver Monitoring System) related research 21 3.1. Research trend of Driver Monitoring System 21 3.2. Driver recognition related research 23 3.2.1. Research related to Driver’s biometric information recognition monitoring system 23 3.2.2. Camera-based Driver Monitoring System 30 4. Driver Monitoring & Emotion Recognition Platform based Technology 32 4.1. Driver Monitoring Hardware Platform based Technology 32 4.1.1. DMS ECU Unit 33 4,1,2. IR Camera-based Technology 40 4.2. Driver Monitoring Software based Technology 43 4.2.1.Driver inattention and drowsiness detection Software based Technology 43 4,2,2. Driver facial expression emotion recognition software based technology 50 5. Implementation of the Proposed Biometric Driver Monitoring System (DMS) Platform 54 5.1. Overview of the proposed camera-based biometric DMS platform 54 5.2. Implementation of the proposed Camera-based DMS Hardware 56 5.2.1. Camera-based DMS ECU Unit Hardware Implementation 58 5.2.2. Camera-based DMS IR Camera Hardware Implementation 62 5.3. Camera-based DMS software platform implementation 64 6. Experiment 70 7. Conclusion 80 REFERENCE 82 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | 생체측정 플랫폼 기반 실시간 차량용 운전자 모니터링 시스템 | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 의용공학과정 | - |
| dc.date.awarded | 2020. 2 | - |
| dc.description.degree | Doctoral | - |
| dc.identifier.localId | 1134011 | - |
| dc.identifier.uci | I804:41038-000000029587 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/common/orgView/000000029587 | - |
| dc.description.alternativeAbstract | This study aims to implement a biometric based real-time driver monitoring and emotion recognition platform for vehicle. In recent years, inadvertent traffic accidents in various driving situations accounted for about 41%[1-2], according to the 2016 Korea Road Traffic Authority statistics and according to the 2025 Roadmap of European New Car Assessment Program Euro NCAP[3], the Driver Monitoring System will be adopted as a safety assessment standard in 2020. In addition, in order to develop autonomous driving, driver intervention is required for certain situations while driving, and research on driver monitoring system (DMS) that can identify drivers beyond detecting simple driver carelessness in order to switch driving control, is ongoing[4]. In this paper, we implemented the Driver Monitoring System that determines and alerts the driver's attention and drowsy driving by analyzing the results of the extracted shape of a driver's face, eyes, nose, mouth, etc[5]. inputted through camera-based image algorithm, and also analyzes driver's emotional state through facial expressions of drivers and categorizes them into joy, sadness, anger, surprise and etc[6-12]. Detailed implementation items are as follows. First, camera-based Driver Monitoring System Hardware platform that satisfies the Automotive Spec is implemented. The DMS hardware platform consists of a DMS unit that handles face recognition, direction, eye opening, and emotion recognition software algorithms and IR Camera[13-15]. Second, Proposed and implemented camera-based driver careless and drowsy driving detection algorithm[16]. To detect the driver's face, Haar-Like and Hough Circle Transform algorithm is used to detect the driver's face, eyes, and mouth area, and then create a Mesh by connecting the detected eye and mouth areas, and detect the driver carelessness according to the change of driver's angle and determine driver’s drowsiness by analyzing the degree of eye closure[19-32]. Vehicle performance recognition rate and image-based frame detection recognition were detected through real vehicle test of driver's carelessness and drowsy driving. Third, a driver emotion recognition algorithm is proposed and implemented by detecting facial expression biometric information[34-37]. AAM (Active Appearance Model) algorithm detects facial expression changes in real time using Landmark of driver's face image and analyzes driver's emotion through 4 facial expressions of driver's joy, sadness, anger, surprise. and then propose service to change music genre of vehicle. Also, measured vehicle performance recognition rate through actual vehicle test[38-45]. Fourth, the vehicle biometric based real-time driver monitoring and emotion recognition platform implemented in this paper consisted of experiment environment by evaluator in actual vehicle situation and evaluated based on actual environment recognition rate. As a result, we could confirm the high performance that can be commercialized, but it is necessary to continuously update and research the image DB learning exposed to various environments and the software algorithm robust design accordingly. | - |
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