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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李明穗(Ming-Sui Lee) | |
| dc.contributor.author | Po-Chen Kuo | en |
| dc.contributor.author | 郭柏辰 | zh_TW |
| dc.date.accessioned | 2021-06-08T00:52:34Z | - |
| dc.date.copyright | 2020-08-20 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-13 | |
| dc.identifier.citation | J.-Y. Lee, P.-H. Han, L. Tsai, R.-D. Peng, Y.-S. Chen, K.-W. Chen, Y.-P. Hung, “Estimating the Simulator Sickness in Immersive Virtual Reality with Optical Flow Analysis,” in SIGGRAPH Asia 2017 Posters, 2017, doi: 10.1145/3145690.3145697. H. G. Kim, W. J. Baddar, H. Lim, H. Jeong, and Y. M. Ro, “Measurement of Exceptional Motion in VR Video Contents for VR Sickness Assessment Using Deep Convolutional Autoencoder,” in Proceedings of the 23rd ACM Symposium on Virtual Reality Software and Technology, 2017, doi: 10.1145/3139131.3139137. S. Lee, S. Kim, H. G. Kim, M. S. Kim, S. Yun, B. Jeong, Y. M. Ro, “Physiological Fusion Net: Quantifying Individual VR Sickness with Content Stimulus and Physiological Response,” in 2019 IEEE International Conference on Image Processing (ICIP), 2019, pp. 440–444, doi: 10.1109/ICIP.2019.8802983. D. K. Jeong, S. Yoo, and Y. Jang, “VR Sickness Measurement with EEG Using DNN Algorithm,” in Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology, 2018, doi: 10.1145/3281505.3283387. T. M. Lee, J. C. Yoon, and I. K. Lee, “Motion Sickness Prediction in Stereoscopic Videos using 3D Convolutional Neural Networks,” IEEE Transactions on Visualization and Computer Graphics, 2019, doi: 10.1109/TVCG.2019.2899186. J. Kim, W. Kim, H. Oh, S. Lee, and S. Lee, “A Deep Cybersickness Predictor Based on Brain Signal Analysis for Virtual Reality Contents,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 10579–10588, doi: 10.1109/ICCV.2019.01068. K. Kim, S. Lee, H. G. Kim, M. Park, and Y. M. Ro, “Deep Objective Assessment Model Based on Spatio-Temporal Perception of 360-Degree Video for VR Sickness Prediction,” in 2019 IEEE International Conference on Image Processing (ICIP), 2019, pp. 3192–3196, doi: 10.1109/ICIP.2019.8803257. R. S. Kennedy, N. E. Lane, K. S. 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Huang, “65‐3: The Quantization of Cybersickness Level Using EEG and ECG for Virtual Reality Head‐Mounted Display,” SID Symposium Digest of Technical Papers, vol. 49, pp. 862–865, Apr. 2018, doi: 10.1002/sdtp.12267. Grosvenor, Theodore P,” Primary Care Optometry., Elsevier Health Sciences., pp. 129–130, Nov. 2014. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/18143 | - |
| dc.description.abstract | VR sickness是阻礙VR市場發展的障礙之一。因此，VR sickness的客觀指標非常重要，可以避免使用者遭受VR sickness。最近，一些研究利用深度學習方法來預測VR sickness。但是，他們的方法需要昂貴的計算資源，這限制了real-time的應用。在本文中，我們提出了從光流導出的混合時間特徵，即horizontal motion strength、vertical motion strength和motion anisotropy。我們建立新的數據集，其中包含二十部5分鐘長的360度影片。在實驗中，每位受試者每分鐘會回答Discomfort Scores（0-10），並在影片結束時填寫模擬器疾病問卷（SSQ）。最後，我們採用隨機森林模型在數據集上進行訓練。該模型使用當前混合時間特徵和先前時段的混合時間特徵，這使得模型考慮了VR sickness之間的時間依賴性。我們的方法分別在PLCC和SROCC上比state-of-the-art高出2%和4%，並且可以real-time運行。 | zh_TW |
| dc.description.abstract | VR sickness is one of the obstacles hindering the development of the VR market. Therefore, the objective metric of VR sickness is very important that can help the user to avoid suffering VR sickness. Recently, some works utilize deep learning methods to predict VR sickness. However, their methods are computationally expensive which limited applications in real-time tasks. In this paper, hybrid temporal features derived from optical flow are proposed, namely horizontal motion strength, vertical motion strength, and motion anisotropy. We introduce a new dataset that contains twenty 5-minute-long 360-degree videos. In the experiment, each subject answers Discomfort Scores (0-10) every minute and performs the Simulator Sickness Questionnaire (SSQ) scores at the end of the video. Finally, a random forest model is adopted to train on our dataset. The model uses the current hybrid temporal features and the previous time period hybrid temporal features that make the model considers temporal dependency between the degree of VR sickness. This method not only outperformed previous state-of-the-art by 2% on PLCC and 4% on SROCC but also runs in real-time. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T00:52:34Z (GMT). No. of bitstreams: 1 U0001-1308202014392500.pdf: 1612870 bytes, checksum: d035bbcba8797bfef217dd60b3ed06e1 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 口試委員會審定書 # 誌謝 I 中文摘要 II ABSTRACT III CONTENTS IV LIST OF FIGURES VI LIST OF TABLES VII CHAPTER 1 INTRODUCTION 1 1.1 BACKGROUND 1 1.2 MAIN CONTRIBUTION 3 CHAPTER 2 RELATED WORK 5 2.1 REDUCING VR SICKNESS 5 2.2 VR SICKNESS PREDICTION 5 CHAPTER 3 PROPOSED METHOD 10 3.1 DATASET 10 3.1.1 Data collection 10 3.1.2 Experimental design 12 3.2 METHOD 13 3.2.1 Gaussian weighted optical flow 14 3.2.2 Hybrid temporal feature 15 3.2.3 Discomfort Score prediction 17 CHAPTER 4 EXPERIMENTAL RESULTS 19 4.1 IMPLEMENTATION DETAILS 19 4.2 SUBJECTIVE EXPERIMENT RESULTS 21 4.3 PERFORMANCE EVALUATION 22 4.4 TIME EVALUATION 26 CHAPTER 5 CONCLUSION 28 5.1 CONCLUSION 28 REFERENCE 29 | |
| dc.language.iso | en | |
| dc.title | 利用先驗資訊和混和時間特徵評估VR動暈症 | zh_TW |
| dc.title | VR Sickness Assessment with Perception Prior and Hybrid Temporal Features | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 梁容輝(Rung-Huei Liang),周承復(Cheng-Fu Chou) | |
| dc.subject.keyword | VR動暈症,數碼動暈症,混合時間特徵,隨機森林,光流法, | zh_TW |
| dc.subject.keyword | VR sickness,cybersickness,hybrid temporal features,random forest,optical flow, | en |
| dc.relation.page | 33 | |
| dc.identifier.doi | 10.6342/NTU202003251 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-08-13 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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| U0001-1308202014392500.pdf 目前未授權公開取用 | 1.58 MB | Adobe PDF |
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