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DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 莊永裕 | zh_TW |
dc.contributor.advisor | Yung-Yu Chuang | en |
dc.contributor.author | 周思佳 | zh_TW |
dc.contributor.author | Si-Jia Zhou | en |
dc.date.accessioned | 2023-05-18T16:50:36Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-05-11 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-02-17 | - |
dc.identifier.citation | [1] M. Alotaibi and B. Alotaibi. Distracted driver classification using deep learning. Signal, Image and Video Processing, 14(3):617–624, 2020.
[2] P. M. Chawan, S. Satardekar, D. Shah, R. Badugu, and A. Pawar. Distracted driver detection and classification. International Journal of Engineering Research and Applications, 4(7), 2018. [3] F. Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251–1258, 2017. [4] J. Cronje and A. P. Engelbrecht. Training convolutional neural networks with class based data augmentation for detecting distracted drivers. In Proceedings of the 9th International Conference on Computer and Automation Engineering, pages 126– 130, 2017. [5] Z. Fang, J. Chen, J. Wang, Z. Wang, N. Liu, and G. Yin. Driver distraction behav- ior detection using a vision transformer model based on transfer learning strategy. In 2022 6th CAA International Conference on Vehicular Control and Intelligence (CVCI), pages 1–6. IEEE, 2022. [6] K. Han, Y. Wang, H. Chen, X. Chen, J. Guo, Z. Liu, Y. Tang, A. Xiao, C. Xu, Y. Xu,et al. A survey on vision transformer. IEEE transactions on pattern analysis and machine intelligence, 45(1):87–110, 2022. [7] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. [8] A. Koesdwiady, S. M. Bedawi, C. Ou, and F. Karray. End-to-end deep learn- ing for driver distraction recognition. In Image Analysis and Recognition: 14th International Conference, ICIAR 2017, Montreal, QC, Canada, July 5–7, 2017, Proceedings 14, pages 11–18. Springer, 2017. [9] E. Li, A. Samat, P. Du, W. Liu, and J. Hu. Improved bilinear cnn model for remote sensing scene classification. IEEE Geoscience and Remote Sensing Letters, 19:1–5, 2020. [10] T.-Y. Lin, A. RoyChowdhury, and S. Maji. Bilinear cnn models for fine-grained visual recognition. In Proceedings of the IEEE international conference on computer vision, pages 1449–1457, 2015. [11] D. Lu and Q. Weng. A survey of image classification methods and techniques for improving classification performance. International journal of Remote sensing, 28(5):823–870, 2007. [12] M. Lu, Y. Hu, and X. Lu. Driver action recognition using deformable and dilated faster r-cnn with optimized region proposals. Applied Intelligence, 50:1100–1111, 2020. [13] S. Masood, A. Rai, A. Aggarwal, M. N. Doja, and M. Ahmad. Detecting distractionof drivers using convolutional neural network. Pattern Recognition Letters, 139:79– 85, 2020. [14] F. Omerustaoglu, C. O. Sakar, and G. Kar. Distracted driver detection by combining in-vehicle and image data using deep learning. Applied Soft Computing, 96:106657, 2020. [15] B. Qin, J. Qian, Y. Xin, B. Liu, and Y. Dong. Distracted driver detection based on a cnn with decreasing filter size. IEEE Transactions on Intelligent Transportation Systems, 23(7):6922–6933, 2021. [16] A. Saluja, J. Xie, and K. Fayazbakhsh. A closed-loop in-process warping detection system for fused filament fabrication using convolutional neural networks. Journal of Manufacturing Processes, 58:407–415, 2020. [17] C.Szegedy,V.Vanhoucke,S.Ioffe,J.Shlens,andZ.Wojna.Rethinkingtheinception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826, 2016. [18] M. Tan and Q. Le. Efficientnet: Rethinking model scaling for convolutional neu- ral networks. In International conference on machine learning, pages 6105–6114. PMLR, 2019. [19] Z. A. Varaich and S. Khalid. Recognizing actions of distracted drivers using in- ception v3 and xception convolutional neural networks. In 2019 2nd International Conference on Advancements in Computational Sciences (ICACS), pages 1–8. IEEE, 2019. [20] Z. Wu, C. Shen, and A. Van Den Hengel. Wider or deeper: Revisiting the resnet model for visual recognition. Pattern Recognition, 90:119–133, 2019. [21] C. Yan, F. Coenen, and B. Zhang. Driving posture recognition by convolutional neural networks. IET Computer Vision, 10(2):103–114, 2016. [22] Z. Zhang and M. Sabuncu. Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in neural information processing systems, 31, 2018. [23] H. Zhao, J. Jia, and V. Koltun. Exploring self-attention for image recogni- tion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10076–10085, 2020. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/87286 | - |
dc.description.abstract | 近年來,道路交通事故的風險正在迅速上升。異常駕駛仍然是交通事故的主 要原因之一。駕駛員異常行為檢測是一個重要的計算機視覺問題,可以在提高交 通安全和減少交通事故方面發揮至關重要的作用。基於卷積神經網絡(CNN)的 廣泛方法已被應用於駕駛員異常駕駛的檢測。在卷積神經網絡 (CNN) 中,卷積運 算擅長提取局部特徵,但難以捕獲全局表示。為解決這一問題,我們提出了一種 新的駕駛員異常行為的檢測方法,該方法將不同的 CNN 模型以及 ViT 模型相結 合來捕獲局部和全局特徵。此外,特徵融合的過程可以大大增強局部特徵的全局 感知能力和全局表示的局部細節。在 StateFarm 數據集上進行的大量實驗表明,我們提出的方法表現出最佳性能。 | zh_TW |
dc.description.abstract | The risk of road accidents has been rising rapidly in recent years. Abnormal driving is still one of the main causes of traffic accidents. Driver abnormal behavior detection is an important computer vision problem that can play a crucial role in improving traffic safety and reducing traffic accidents. A wide range of methods based on convolutional neural networks (CNNs) have been applied to the detection of abnormal driving by drivers. In convolutional neural networks (CNNs), convolution operations are good at extracting local features, but struggle to capture global representations. To address this problem , we propose a novel driver abnormal behavior detection method that combines different CNN models and ViT models to capture local and global features. In addition, the process of feature fusion can greatly enhance the global awareness of local features and the local details of the global representation. Extensive experiments on the StateFarm dataset show that our proposed method exhibits the best performance. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-05-18T16:50:36Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-05-18T16:50:36Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i
Acknowledgements ii 摘要 iii Abstract iv Contents v List of Figures vii List of Tables viii Chapter 1 Introduction 1 Chapter 2 Related work 3 2.1 Driver Distraction Detection 3 2.2 Bilinear CNN Model 4 Chapter 3 Method 5 3.1 Multi-Model Fusion Network 5 3.2 CNN Feature Module 6 3.3 Transformer Feature Module 7 3.4 Feature Fusion Module 8 Chapter 4 Experiment and Result 9 4.1 Dataset 9 4.2 Experiment Setup 10 4.3 Evaluation Metrics 10 4.4 Main Results 11 4.5 Ablation Study 12 Chapter 5 Conclusion 14 Reference 15 | - |
dc.language.iso | zh_TW | - |
dc.title | 使用多模型特徵進行駕駛員異常行為檢測 | zh_TW |
dc.title | Distracted Driver Detection Using Multi-model Features | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-1 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 吳賦哲;葉正聖 | zh_TW |
dc.contributor.oralexamcommittee | BIn-Zhe Wu;Zheng-Sheng Ye | en |
dc.subject.keyword | 駕駛員異常行為檢測,特徵融合,ViT 模型,多模型特徵, | zh_TW |
dc.subject.keyword | Distracted Driver Detection,Feature Fusion,Vision Transformer,Multi- model Features, | en |
dc.relation.page | 18 | - |
dc.identifier.doi | 10.6342/NTU202300564 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2023-02-18 | - |
dc.contributor.author-college | 電機資訊學院 | - |
dc.contributor.author-dept | 資訊工程學系 | - |
顯示於系所單位: | 資訊工程學系 |
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