請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88656完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪一平 | zh_TW |
| dc.contributor.advisor | Yi-Ping Hung | en |
| dc.contributor.author | 曾筱晴 | zh_TW |
| dc.contributor.author | Hsiao-Ching Tseng | en |
| dc.date.accessioned | 2023-08-15T17:14:43Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-15 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-03 | - |
| dc.identifier.citation | Nir Aharon, Roy Orfaig, and Ben-Zion Bobrovsky. Bot-sort: Robust associations multi-pedestrian tracking. arXiv preprint arXiv:2206.14651, 2022.
David-Alexandre Beaupré, Guillaume-Alexandre Bilodeau, and Nicolas Saunier. Improving multiple object tracking with optical flow and edge preprocessing. arXiv preprint arXiv:1801.09646, 2018. Luca Bertinetto, Jack Valmadre, Joao F Henriques, Andrea Vedaldi, and Philip HS Torr. Fully-convolutional siamese networks for object tracking. In Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part II 14, pages 850–865. Springer, 2016. Goutam Bhat, Martin Danelljan, Luc Van Gool, and Radu Timofte. Learning discriminative model prediction for tracking. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6182–6191, 2019. Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934, 2020. Antonio Brunetti, Domenico Buongiorno, Gianpaolo Francesco Trotta, and Vitoantonio Bevilacqua. Computer vision and deep learning techniques for pedestrian detection and tracking: A survey. Neurocomputing, 300:17–33, 2018. Ziang Cao, Changhong Fu, Junjie Ye, Bowen Li, and Yiming Li. Hift: Hierarchical feature transformer for aerial tracking. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 15457–15466, October 2021. Ziang Cao, Changhong Fu, Junjie Ye, Bowen Li, and Yiming Li. Hift: Hierarchical feature transformer for aerial tracking. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 15457–15466, October 2021. Ziang Cao, Changhong Fu, Junjie Ye, Bowen Li, and Yiming Li. Siamapn++: Siamese attentional aggregation network for real-time uav tracking. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3086–3092. IEEE, 2021. Ziang Cao, Ziyuan Huang, Liang Pan, Shiwei Zhang, Ziwei Liu, and Changhong Fu. TCTrack: Temporal Contexts for Aerial Tracking. In CVPR, pages 14798–14808, 2022. Boyu Chen, Peixia Li, Lei Bai, Lei Qiao, Qiuhong Shen, Bo Li, Weihao Gan, Wei Wu, and Wanli Ouyang. Backbone is all your need: a simplified architecture for visual object tracking. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXII, pages 375–392. Springer, 2022. Jia Chen, Yibo Ai, Yuhan Qian, and Weidong Zhang. A novel siamese attention network for visual object tracking of autonomous vehicles. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 235(10-11):2764–2775, 2021. Ping-Yang Chen, Ming-Ching Chang, Jun-Wei Hsieh, and Yong-Sheng Chen. Parallel residual bi-fusion feature pyramid network for accurate single-shot object detection. IEEE Transactions on Image Processing, 30:9099–9111, 2021. Xin Chen, Houwen Peng, Dong Wang, Huchuan Lu, and Han Hu. Seqtrack: Sequence to sequence learning for visual object tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14572–14581, 2023. Xin Chen, Bin Yan, Jiawen Zhu, Dong Wang, Xiaoyun Yang, and Huchuan Lu. Transformer tracking. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8126–8135, 2021. Yu-Hsi Chen, Chien-Yao Wang, Cheng-Yun Yang, Hung-Shuo Chang, Youn-Long Lin, Yung-Yu Chuang, and Hong-Yuan Mark Liao. Neighbortrack: Improving single object tracking by bipartite matching with neighbor tracklets. arXiv preprint arXiv:2211.06663, 2022. Yutao Cui, Cheng Jiang, Limin Wang, and Gangshan Wu. Mixformer: End-to-end tracking with iterative mixed attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13608–13618, 2022. Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, and Michael Felsberg. Atom: Accurate tracking by overlap maximization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4660–4669, 2019. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao, and Haibin Ling. Lasot: A high-quality benchmark for large-scale single object tracking. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5374–5383, 2019. Cheng-Yang Fu, Wei Liu, Ananth Ranga, Ambrish Tyagi, and Alexander C Berg. Dssd: Deconvolutional single shot detector. arXiv preprint arXiv:1701.06659, 2017. Shenyuan Gao, Chunluan Zhou, Chao Ma, Xinggang Wang, and Junsong Yuan. Aiatrack: Attention in attention for transformer visual tracking. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXII, pages 146–164. Springer, 2022. Shenyuan Gao, Chunluan Zhou, and Jun Zhang. Generalized relation modeling for transformer tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18686–18695, 2023. Ross Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440–1448, 2015. Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 580–587, 2014. Haifeng Gong, Jack Sim, Maxim Likhachev, and Jianbo Shi. Multi-hypothesis motion planning for visual object tracking. In 2011 International Conference on Computer Vision, pages 619–626. IEEE, 2011. Junya Hayashida, Kazuya Nishimura, and Ryoma Bise. Mpm: Joint representation of motion and position map for cell tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3823–3832, 2020. Kaijie He, Canlong Zhang, Sheng Xie, Zhixin Li, and Zhiwen Wang. Target-aware tracking with long-term context attention. arXiv preprint arXiv:2302.13840, 2023. Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16000–16009, 2022. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence, 37(9):1904–1916, 2015. David Held, Sebastian Thrun, and Silvio Savarese. Learning to track at 100 fps with deep regression networks. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, PartI 14, pages 749–765. Springer, 2016. Lianghua Huang, Xin Zhao, and Kaiqi Huang. Got-10k: A large high-diversity benchmark for generic object tracking in the wild. IEEE transactions on pattern analysis and machine intelligence, 43(5):1562–1577, 2019. Zulaikha Kadim, Mohd Asyraf Zulkifley, and Nabilah Hamzah. Deep-learning based single object tracker for night surveillance. International Journal of Electrical & Computer Engineering (2088-8708), 10(4), 2020. Hei Law and Jia Deng. Cornernet: Detecting objects as paired keypoints. In Proceedings of the European conference on computer vision (ECCV), pages 734–750, 2018. Bo Li, Wei Wu, Qiang Wang, Fangyi Zhang, Junliang Xing, and Junjie Yan. Siamrpn++: Evolution of siamese visual tracking with very deep networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4282–4291, 2019. Bo Li, Junjie Yan, Wei Wu, Zheng Zhu, and Xiaolin Hu. High performance visual tracking with siamese region proposal network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8971–8980, 2018. Yiming Li, Changhong Fu, Fangqiang Ding, Ziyuan Huang, and Geng Lu. Autotrack: Towards high-performance visual tracking for uav with automatic spatiotemporal regularization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11923–11932, 2020. Zuoxin Li and Fuqiang Zhou. Fssd: feature fusion single shot multibox detector. arXiv preprint arXiv:1712.00960, 2017. Liting Lin, Heng Fan, Zhipeng Zhang, Yong Xu, and Haibin Ling. Swintrack: A simple and strong baseline for transformer tracking. Advancesin Neural Information Processing Systems, 35:16743–16754, 2022. Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740–755. Springer, 2014. Shu Liu, Lu Qi, Haifang Qin, Jianping Shi, and Jiaya Jia. Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8759–8768, 2018. Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. Ssd: Single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pages 21–37. Springer, 2016. Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. Matthias Mueller, Neil Smith, and Bernard Ghanem. A benchmark and simulator for uav tracking. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pages 445–461. Springer, 2016. Matthias Muller, Adel Bibi, Silvio Giancola, Salman Alsubaihi, and Bernard Ghanem. Trackingnet: A large-scale dataset and benchmark for object tracking in the wild. In Proceedings of the European Conference on computer vision (ECCV), pages 300–317, 2018. Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779–788, 2016. Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018. Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards realtime object detection with region proposal networks. Advancesin neural information processing systems, 28, 2015. Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, and Silvio Savarese. Generalized intersection over union: A metric and a loss for bounding box regression. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 658–666, 2019. Mikel Rodriguez, Saad Ali, and Takeo Kanade. Tracking in unstructured crowded scenes. In 2009 IEEE 12th International Conference on Computer Vision, pages 1389–1396. IEEE, 2009. Jasper RR Uijlings, Koen EA Van De Sande, Theo Gevers, and Arnold WM Smeulders. Selective search for object recognition. International journal of computer vision, 104:154–171, 2013. Jack Valmadre, Luca Bertinetto, Joao Henriques, Andrea Vedaldi, and Philip HS Torr. End-to-end representation learning for correlation filter based tracking. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2805–2813, 2017. Leon Amadeus Varga, Benjamin Kiefer, Martin Messmer, and Andreas Zell. Seadronessee: A maritime benchmark for detecting humans in open water. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 2260–2270, January 2022. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7464–7475, 2023. Chien-Yao Wang, Hong-Yuan Mark Liao, Yueh-Hua Wu, Ping-Yang Chen, Jun-Wei Hsieh, and I-Hau Yeh. Cspnet: A new backbone that can enhance learning capability of cnn. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 390–391, 2020. Chien-Yao Wang, Hong-Yuan Mark Liao, and I-Hau Yeh. Designing network design strategies through gradient path analysis. arXiv preprint arXiv:2211.04800, 2022. Ning Wang, Wengang Zhou, Jie Wang, and Houqiang Li. Transformer meets tracker: Exploiting temporal context for robust visual tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1571–1580, 2021. Yu-Hsiang Wang. Smiletrack: Similarity learning for multiple object tracking. arXiv preprint arXiv:2211.08824, 2022. Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon. Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV), pages 3–19, 2018. Y. Wu, J. Lim, and M. Yang. Object tracking benchmark. IEEE Transactions on Pattern Analysis & Machine Intelligence, 37(09):1834–1848, sep 2015. Fei Xie, Chunyu Wang, Guangting Wang, Yue Cao, Wankou Yang, and Wenjun Zeng. Correlation-aware deep tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8751–8760, 2022. Yinda Xu, Zeyu Wang, Zuoxin Li, Ye Yuan, and Gang Yu. Siamfc++: Towards robust and accurate visual tracking with target estimation guidelines. In Proceedings of the AAAI conference on artificial intelligence, pages 12549–12556, 2020. Shinya Yamamoto, Yasushi Mae, Yoshiaki Shirai, and Jun Miura. Realtime multiple object tracking based on optical flows. In Proceedings of 1995 IEEE International Conference on Robotics and Automation, volume 3, pages 2328–2333. IEEE, 1995. Bin Yan, Houwen Peng, Jianlong Fu, Dong Wang, and Huchuan Lu. Learning spatio-temporal transformer for visual tracking. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 10448–10457, October 2021. Tianyu Yang and Antoni B Chan. Learning dynamic memory networks for object tracking. In Proceedings of the European conference on computer vision (ECCV), pages 152–167, 2018. Botao Ye, Hong Chang, Bingpeng Ma, Shiguang Shan, and Xilin Chen. Joint feature learning and relation modeling for tracking: A one-stream framework. In ECCV, 2022. Bin Yu, Ming Tang, Linyu Zheng, Guibo Zhu, Jinqiao Wang, Hao Feng, Xuetao Feng, and Hanqing Lu. High-performance discriminative tracking with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9856–9865, 2021. Lichao Zhang, Abel Gonzalez-Garcia, Joost van de Weijer, Martin Danelljan, and Fahad Shahbaz Khan. Learning the model update for siamese trackers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 4010–4019, 2019. Yifu Zhang, Peize Sun, Yi Jiang, Dongdong Yu, Fucheng Weng, Zehuan Yuan, Ping Luo, Wenyu Liu, and Xinggang Wang. Bytetrack: Multi-object tracking by associating every detection box. In European Conference on Computer Vision, pages 1–21. Springer, 2022. Zhipeng Zhang, Houwen Peng, Jianlong Fu, Bing Li, and Weiming Hu. Ocean: Object-aware anchor-free tracking. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXI 16, pages 771–787. Springer, 2020. Xingyi Zhou, Dequan Wang, and Philipp Krähenbühl. Objects as points. arXiv preprint arXiv:1904.07850, 2019. Pengfei Zhu, Longyin Wen, Dawei Du, Xiao Bian, Heng Fan, Qinghua Hu, and Haibin Ling. Detection and tracking meet drones challenge. IEEE Transactions on Pattern Analysis and Machine Intel Machine Intelligence, pages 1–1, 2021. Zheng Zhu, Qiang Wang, Bo Li, Wei Wu, Junjie Yan, and Weiming Hu. Distractoraware siamese networks for visual object tracking. In Proceedings of the European conference on computer vision (ECCV), pages 101–117, 2018. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88656 | - |
| dc.description.abstract | 空間和時間要素對於物體追蹤至關重要，特别是對於目標外觀隨時間不斷變化的長期物體追蹤。除了定義目標的初始模板外，動態模板能處理外觀變化，並在精心挑選的情況下提高追蹤的準確度。在本論文中，我們提出了一種名為 OSTrack-ADT 的單流追蹤器，該追蹤器具有自適應動態模板更新機制。我們提出的更新策略會在每一幀中觸發，並利用過去的追蹤分數資訊來動態調整閾值。與以往方法不同，我們設計了一個新穎的分數解碼器，用於預測每個邊界框提案的交集與聯集比值（IoU），而非使用類別分數作為信心指數，以抑制部分遮擋或錯誤邊界框的影響。當可信的模板其時空懲罰的 IoU 分數大於動態閾值，則取代當前的動態模板。實驗結果顯示，我們所提出的追蹤器能達到每秒 65 幀的實時運行速度，在長期追蹤基準上取得了巨大的改進，同時在中期和短期追蹤基準上保持了具有競爭力的性能。此外，提出的 OSTrack-ADT 也應用於實時的無人機追蹤系統。 | zh_TW |
| dc.description.abstract | Spatial and temporal factors are critical for object tracking, especially for long-term object tracking that contains the target's appearance change over time. Besides the initial template which defines the target, the dynamic template handles appearance variation and improves the tracking accuracy if it is cherry-picked. In this thesis, we propose OSTrack-ADT, a One-Stream Tracker with an Adaptive Dynamic Template updating mechanism. Our update decision is triggered every frame based on a dynamic threshold that considers the historical information of tracking confidence. Different from previous approaches, we devise a novel score head to predict the Intersection over Union (IoU) score of each bounding box proposal instead of utilizing the classification score as the confidence score to suppress the impact of the partial occlusion or erroneous bounding boxes. The current dynamic template will be replaced if there is a plausible one whose spatiotemporally penalized IoU score is greater than the dynamic threshold. The experimental results show that the proposed tracker operates in real-time (65fps) and makes a great improvement on the long-term tracking benchmark while maintaining comparable performance on mid-term and short-term tracking benchmarks. Furthermore, our OSTrack-ADT is also applied to a real-time unmanned aerial vehicle tracking system. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T17:14:43Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-15T17:14:43Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgements iii
摘要 v Abstract vii Contents ix List of Figures xiii List of Tables xv Chapter 1 Introduction 1 Chapter 2 Related Work 5 2.1 Object Tracking 5 2.1.1 Single Object Tracking 5 2.1.1.1 Two-Stage v.s. One-Stage Frameworks 6 2.1.1.2 Tracking with Temporal Factors 8 2.1.2 Multiple Object Tracking 10 2.1.2.1 Detection-free Tracking 10 2.1.2.2 Detection-based Tracking 10 2.2 Object Detection 11 Chapter 3 Methodology 15 3.1 OSTrack 15 3.1.1 Linear Projection 15 3.1.2 Transformer Encoder Layers with Early Candidate Elimination Module 16 3.1.3 Prediction Head 17 3.2 OSTrack-ADT 18 3.2.1 Input 18 3.2.2 Score Head 18 3.3 Online Update Strategy 20 3.3.1 Dynamic Threshold 21 3.3.2 IoU Score with Spatial-Temporal Penalty 22 3.4 Learning Objective 23 Chapter 4 Experiments 25 4.1 Implementation Details 25 4.2 Evaluation Metrics 27 4.3 Comparison with the State-of-the-art Trackers 28 4.4 Ablation Studies 29 4.4.1 Qualitative Results 30 4.4.2 Update Strategies 31 4.4.3 Model Architecture 32 4.4.4 Loss Function 33 Chapter 5 Real-Time UAV Tracking System 35 5.1 System Architecture 35 5.1.1 Detector 36 5.1.2 Selection Module 37 5.1.3 Tracker 38 5.2 Update Strategy 38 5.3 System Demonstration 39 5.3.1 Tracking Modes 40 5.3.2 Qualitative Results 40 5.3.3 Discussion 48 Chapter 6 Conclusion & Future Works 51 References 53 | - |
| dc.language.iso | en | - |
| dc.title | 自適應動態模板目標追蹤及其在無人機系統中的應用 | zh_TW |
| dc.title | Object Tracking with an Adaptive Dynamic Template and Its Application to an Unmanned Aerial Vehicle System | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 陳祝嵩;王鈺強 ;花凱龍;徐繼聖 | zh_TW |
| dc.contributor.oralexamcommittee | Chu-Song Chen;Yu-Chiang Wang;Kai-Lung Hua;Gee-Sern Hsu | en |
| dc.subject.keyword | 物體追蹤,動態模板,線上更新策略,追蹤系統, | zh_TW |
| dc.subject.keyword | Object Tracking,Dynamic Template,Online Update Strategy,Tracking System, | en |
| dc.relation.page | 63 | - |
| dc.identifier.doi | 10.6342/NTU202302292 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-08-07 | - |
| 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 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-111-2.pdf 目前未授權公開取用 | 57.5 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。