Skip navigation

DSpace

機構典藏 DSpace 系統致力於保存各式數位資料(如:文字、圖片、PDF)並使其易於取用。

點此認識 DSpace
DSpace logo
English
中文
  • 瀏覽論文
    • 校院系所
    • 出版年
    • 作者
    • 標題
    • 關鍵字
    • 指導教授
  • 搜尋 TDR
  • 授權 Q&A
    • 我的頁面
    • 接受 E-mail 通知
    • 編輯個人資料
  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 資訊網路與多媒體研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94436
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor洪一平zh_TW
dc.contributor.advisorYi-Ping Hungen
dc.contributor.author戴靖婷zh_TW
dc.contributor.authorChing-Ting Taien
dc.date.accessioned2024-08-15T17:29:15Z-
dc.date.available2024-08-16-
dc.date.copyright2024-08-15-
dc.date.issued2024-
dc.date.submitted2024-08-10-
dc.identifier.citationN. Aharon, R. Orfaig, and B.-Z. Bobrovsky. Bot-sort: Robust associations multi- pedestrian tracking. arXiv preprint arXiv:2206.14651, 2022.
R. Al-batat, A. Angelopoulou, S. Premkumar, J. Hemanth, and E. Kapetanios. An end-to-end automated license plate recognition system using yolo based vehicle and license plate detection with vehicle classification. Sensors, 22(23), 2022.
K. al Hazmi. 台灣第 7 代通用車輛號牌簡介, Jun 2019.
N. Balamuralidhar, S. Tilon, and F. Nex. Multeye: Monitoring system for real- time vehicle detection, tracking and speed estimation from uav imagery on edge- computing platforms. Remote Sensing, 13(4), 2021.
D. Beaupré, G. Bilodeau, and N. Saunier. Improving multiple object tracking with optical flow and edge preprocessing. CoRR, abs/1801.09646, 2018.
A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft. Simple online and realtime tracking. In 2016 IEEE International Conference on Image Processing (ICIP), pages 3464–3468, 2016.
D. Biswas, H. Su, C. Wang, and A. Stevanovic. Speed estimation of multiple moving objects from a moving uav platform. ISPRS International Journal of Geo-Information, 8(6), 2019.
A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao. Yolov4: Optimal speed and accu- racy of object detection, 2020.
H. Chen, X. Gao, H. Li, and Z. Yang. A framework for the optimal deployment of police drones based on street-level crime risk. Applied Geography, 162:103178, 2024.
A. Cvijetić, S. Djukanović, and A. Peruničić. Deep learning-based vehicle speed estimation using the yolo detector and 1d-cnn. In 2023 27th International Conference on Information Technology (IT), pages 1–4, 2023.
D.Du,P.Zhu,L.Wen,X.Bian,H.Lin,Q.Hu,T.Peng,J.Zheng,X.Wang,Y.Zhang, L. Bo, H. Shi, R. Zhu, A. Kumar, A. Li, A. Zinollayev, A. Askergaliyev, A. Schu- mann, B. Mao, B. Lee, C. Liu, C. Chen, C. Pan, C. Huo, D. Yu, D. Cong, D. Zeng, D. R. Pailla, D. Li, D. Wang, D. Cho, D. Zhang, F. Bai, G. Jose, G. Gao, G. Liu, H. Xiong, H. Qi, H. Wang, H. Qiu, H. Li, H. Lu, I. Kim, J. Kim, J. Shen, J. Lee, J. Ge, J. Xu, J. Zhou, J. Meier, J. W. Choi, J. Hu, J. Zhang, J. Huang, K. Huang, K. Wang, L. Sommer, L. Jin, L. Zhang, L. Huang, L. Sun, L. Steinmann, M. Jia, N. Xu, P. Zhang, Q. Chen, Q. Lv, Q. Liu, Q. Cheng, S. S. Chennamsetty, S. Chen, S. Wei, S. S. S. Kruthiventi, S. Hong, S. Kang, T. Wu, T. Feng, V. A. Kollerathu, W. Li, W. Dai, W. Qin, W. Wang, X. Wang, X. Chen, X. Chen, X. Sun, X. Zhang, X. Zhao, X. Zhang, X. Zhang, X. Chen, X. Wei, X. Zhang, Y. Li, Y. Chen, Y. H. Toh, Y. Zhang, Y. Zhu, Y. Zhong, Z. Wang, Z. Wang, Z. Song, and Z. Liu. Visdrone-det2019: The vision meets drone object detection in image challenge results. In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pages 213–226, 2019.
Y. Du, Z. Zhao, Y. Song, Y. Zhao, F. Su, T. Gong, and H. Meng. Strongsort: Make deepsort great again. IEEE Transactions on Multimedia, 2023.
C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg. Dssd: Deconvolutional single shot detector. arXiv preprint arXiv:1701.06659, 2017.
R. Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440–1448, 2015.
R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for ac- curate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 580–587, 2014.
M. L. Hoang. Smart drone surveillance system based on ai and on iot communication in case of intrusion and fire accident. Drones, 7(12), 2023.
G.-S. Hsu, J.-C. Chen, and Y.-Z. Chung. Application-oriented license plate recogni- tion. IEEE transactions on vehicular technology, 2012.
JaidedAI. Easyocr. https://github.com/JaidedAI/EasyOCR, 2020.
Y. Jamtsho, P. Riyamongkol, and R. Waranusast. Real-time license plate detection for non-helmeted motorcyclist using yolo. ICT Express, 7(1):104–109, 2021.
R. Laroca, E. Severo, L. A. Zanlorensi, L. Oliveira, G. R. Gonçalves, W. R. Schwartz, and D. Menotti. A robust real-time automatic license plate recognition based on the yolo detector. 2018 International Joint Conference on Neural Networks (IJCNN), pages 1–10, 2018.
R. Laroca, E. Severo, L. A. Zanlorensi, L. S. Oliveira, G. R. Gonçalves, W. R. Schwartz, and D. Menotti. A robust real-time automatic license plate recognition based on the YOLO detector. In International Joint Conference on Neural Networks (IJCNN), pages 1–10, July 2018.
R. Laroca, L. Zanlorensi, G. Gonçalves, E. Todt, W. Schwartz, and D. Menotti. An efficient and layout‐independent automatic license plate recognition system based on the yolo detector. IET Intelligent Transport Systems, 15:483–503, 02 2021.
J. Li, S. Chen, F. Zhang, E. Li, T. Yang, and Z. Lu. An adaptive framework for multi-vehicle ground speed estimation in airborne videos. Remote Sensing, 11(10), 2019.
Z. Li, L. Yang, and F. Zhou. Fssd: feature fusion single shot multibox detector. arXiv preprint arXiv:1712.00960, 2017.
T. Lin, M. Maire, S. J. Belongie, L. D. Bourdev, R. B. Girshick, J. Hays, P. Perona, D. Ramanan, P. Doll’a r, and C. L. Zitnick. Microsoft COCO: common objects in context. CoRR, abs/1405.0312, 2014.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg. SSD: Single Shot MultiBox Detector, page 21–37. Springer International Publishing, 2016.
Z. Liu and J. Li. Application of unmanned aerial vehicles in precision agriculture. Agriculture, 13(7), 2023.
Z. Liu, X. Wang, C. Wang, W. Liu, and X. Bai. Sparsetrack: Multi-object track- ing by performing scene decomposition based on pseudo-depth. arXiv preprint arXiv:2306.05238, 2023.
D. C. Luvizon, B. T. Nassu, and R. Minetto. Vehicle speed estimation by license plate detection and tracking. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6563–6567, 2014.
Z. Mahmood, K. Khan, U. Khan, S. H. Adil, S. S. A. Ali, and M. Shahzad. Towards automatic license plate detection. Sensors, 22(3), 2022.
L. Matlekovic and P. Schneider-Kamp. From monolith to microservices: Software architecture for autonomous uav infrastructure inspection. In Embedded Systems and Applications, EMSA 2022. Academy and Industry Research Collaboration Center (AIRCC), Mar. 2022.
S.-Y. Nabavi-Chashmi, D. Asadi, and K. Ahmadi. Image-based uav position and velocity estimation using a monocular camera. Control Engineering Practice, 134:105460, 2023.
M. Ning, X. Ma, Y. Lu, S. Calderara, and R. Cucchiara. Seefar: Vehicle speed esti- mation and flow analysisfrom a moving uav. In Image Analysis and Processing–ICIAP 2022: 21st International Conference, Lecce, Italy, May 23–27, 2022, Proceedings, Part III, page 278–289, Berlin, Heidelberg, 2022. Springer-Verlag.
A. Peruničić, S. Djukanović, and A. Cvijetić. Vision-based vehicle speed estima- tion using the yolo detector and rnn. In 2023 27th International Conference on Information Technology (IT), pages 1–4, 2023.
H. C. Quang, T. Do Thanh, and C. T. Van. Character time-series matching for robust license plate recognition. In 2022 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), pages 1–6. IEEE, 2022.
T. Rahman, M. A. L. Siregar, A. Kurniawan, S. Juniastuti, and E. M. Yu- niarno. Vehicle speed calculation from drone video based on deep learning. In 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM), pages 229–233, 2020.
A. Raja, L. Njilla, and J. Yuan. Blur the eyes of uav: Effective attacks on uav-based infrastructure inspection. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), pages 661–665, 2021.
J. Redmon, S. Divvala, R. Girshick, and A. 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.
J. Redmon and A. Farhadi. Yolo9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7263–7271, 2017.
J. Redmon and A. Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018.
A. Rejeb, A. Abdollahi, K. Rejeb, and H. Treiblmaier. Drones in agriculture: A review and bibliometric analysis. Computers and Electronics in Agriculture, 198:107017, 2022.
S. Ren, K. He, R. Girshick, and J. Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015.
P. Royo, A. Asenjo, J. Trujillo, E. Çetin, and C. Barrado. Enhancing drones for law enforcement and capacity monitoring at open large events. Drones, 6(11), 2022.
S. M. Silva and C. R. Jung. License plate detection and recognition in unconstrained scenarios. In Computer Vision–ECCV 2018: 15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part XII, page 593–609, Berlin, Heidelberg, 2018. Springer-Verlag.
A. Singh, D. Patil, and S. Omkar. Eye in the sky: Real-time drone surveillance sys- tem (dss) for violent individuals identification using scatternet hybrid deep learn- ing network. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 1710–17108, 2018.
R.Smith.Anoverviewofthetesseractocrengine.InNinthInternationalConference on Document Analysis and Recognition (ICDAR 2007), volume 2, pages 629–633, 2007.
D. Vedhaviyassh, R. Sudhan, G. Saranya, M. Safa, and D. Arun. Comparative analysis of easyocr and tesseractocr for automatic license plate recognition using deep learning algorithm. In 2022 6th International Conference on Electronics, Communication and Aerospace Technology, pages 966–971, 2022.
A. Wang, H. Chen, L. Liu, K. Chen, Z. Lin, J. Han, and G. Ding. Yolov10: Real-time end-to-end object detection. arXiv preprint arXiv:2405.14458, 2024.
C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. 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.
N. Wojke, A. Bewley, and D. Paulus. Simple online and real time tracking with a deep association metric. In 2017 IEEE International Conference on Image Processing (ICIP), pages 3645–3649. IEEE, 2017.
Z. Xu, W. Yang, A. Meng, N. Lu, H. Huang, C. Ying, and L. Huang. Towards end-to-end license plate detection and recognition: A large dataset and baseline. In Computer Vision–ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XIII, page 261–277, Berlin, Heidelberg, 2018. Springer-Verlag.
B. Yan, Y. Jiang, P. Sun, D. Wang, Z. Yuan, P. Luo, and H. Lu. Towards grand unification of object tracking. In S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, and T. Hassner, editors, Computer Vision – ECCV 2022, pages 733–751, Cham, 2022. Springer Nature Switzerland.
K. Yi, K. Luo, X. Luo, J. Huang, H. Wu, R. Hu, and W. Hao. Ucmctrack: Multi- object tracking with uniform camera motion compensation. Proceedings of the AAAI Conference on Artificial Intelligence, 38(7):6702–6710, Mar. 2024.
Z. Zaheer, A. Usmani, E. Khan, and M. A. Qadeer. Aerial surveillance system using uav. In 2016 Thirteenth International Conference on Wireless and Optical Communications Networks (WOCN), pages 1–7, 2016.
Y. Zhang, P. Sun, Y. Jiang, D. Yu, Z. Yuan, P. Luo, W. Liu, and X. Wang. Bytetrack: Multi-object tracking by associating every detection box. In European Conference on Computer Vision, 2021.
S. Zherzdev and A. Gruzdev. Lprnet: License plate recognition via deep neural networks. arXiv preprint arXiv:1806.10447, 2018.
-
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94436-
dc.description.abstract本研究旨在通過擴大覆蓋範圍和即時監控來提升道路安全,探討無人機輔助的超速執法應用。
所提出的系統包括幾個關鍵模組:車輛偵測與多目標追蹤、基於車道線段的速度估測方法,以及車牌辨識。為了確保系統的穩健性和準確性,創建了模擬數據以評估速度估測算法的準確性。此外,還創建了一個臺灣車牌的合成數據集,用於訓練和辨識臺灣車牌。另外,收集無人機數據以評估速度估測方法和車牌辨識的準確性。這個系統基於無人機捕捉圖像,並利用自收集數據進行評估,解決了無人機技術在速度限制執法中的挑戰,旨在確保有效部署並顯著促進整體道路安全的改善。
zh_TW
dc.description.abstractAiming to enhance road safety through broader coverage and real-time monitoring, this study investigates the implementation of drone-assisted speed law enforcement applications. The proposed system comprises several key modules: detection and multi-object tracking of vehicles, a lane-line segment-based method for speed estimation, and license plate recognition. To ensure the robustness and accuracy of the system, Simulated data is generated to evaluate the speed estimation algorithms. Additionally, a synthetic dataset of Taiwanese license plates is developed for training recognition models. Drone data is collected to evaluate speed estimation methodologies and the accuracy of license plate recognition. This comprehensive system, based on drone-captured images and utilizing self-collected data for evaluation, addresses the challenges associated with drone technology in speed limit enforcement, ensuring effective deployment and significantly contributing to overall road safety improvements.en
dc.description.provenanceSubmitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-15T17:29:15Z
No. of bitstreams: 0
en
dc.description.provenanceMade available in DSpace on 2024-08-15T17:29:15Z (GMT). No. of bitstreams: 0en
dc.description.tableofcontentsAcknowledgements i
摘要 ii
Abstract iii
Contents iv
List of Figures vii
List of Tables viii
Chapter 1 Introduction 1
Chapter 2 Related Work 5
2.1 Object Detection 5
2.2 Multi-object Tracking 7
2.3 Speed Estimation 8
2.4 License Plate Recognition 9
Chapter 3 Proposed Method 11
3.1 System Architecture 11
3.2 Object Detection 13
3.3 Multi-object Tracking 14
3.4 Speed Estimation 15
3.5 License Plate Recognition 18
3.5.1 Taiwanese License Plate Recognition 20
Chapter 4 Experiments 24
4.1 Dataset 24
4.1.1 Simulated Dataset 24
4.1.2 Real-world Dataset 25
4.1.3 Training Dataset 27
4.1.3.1 Object Detection 27
4.1.3.2 License Plate Recognition 29
4.2 Implementation Details 30
4.3 Speed Estimation 32
4.3.1 Evaluation Metrics 32
4.3.2 Simulated Dataset 34
4.3.3 Real-world Dataset 36
4.4 License Plate Recognition 37
4.4.1 Evaluation Metrics 37
4.4.2 Mixed Dataset 38
4.4.3 Drone-captured Dataset 39
4.5 System Demonstration 42
Chapter 5 Discussion and Analysis 45
5.1 Speed Estimation 45
5.2 License Plate Recognition 47
Chapter 6 Conclusion 51
References 54
-
dc.language.isoen-
dc.subject速度估測zh_TW
dc.subject車牌辨識zh_TW
dc.subject科技執法zh_TW
dc.subject物體偵測zh_TW
dc.subject多目標追蹤zh_TW
dc.subject無人機zh_TW
dc.subjectDronesen
dc.subjectTechnological Law Enforcementen
dc.subjectObject Detectionen
dc.subjectMulti-object Trackingen
dc.subjectSpeed Estimationen
dc.subjectLicense Plate Recognitionen
dc.title無人機輔助物體偵測與追蹤技術在科技執法中的應用zh_TW
dc.titleDrone-Assisted Object Detection and Tracking for Technological Law Enforcement Applicationsen
dc.typeThesis-
dc.date.schoolyear112-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee李明穗;蘇柏齊;林維暘zh_TW
dc.contributor.oralexamcommitteeMing-Sui Lee;Po-Chyi Su;Wei-Yang Linen
dc.subject.keyword無人機,科技執法,物體偵測,多目標追蹤,速度估測,車牌辨識,zh_TW
dc.subject.keywordDrones,Technological Law Enforcement,Object Detection,Multi-object Tracking,Speed Estimation,License Plate Recognition,en
dc.relation.page61-
dc.identifier.doi10.6342/NTU202403555-
dc.rights.note未授權-
dc.date.accepted2024-08-13-
dc.contributor.author-college電機資訊學院-
dc.contributor.author-dept資訊網路與多媒體研究所-
顯示於系所單位:資訊網路與多媒體研究所

文件中的檔案:
檔案 大小格式 
ntu-112-2.pdf
  未授權公開取用
16.64 MBAdobe PDF
顯示文件簡單紀錄


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。

社群連結
聯絡資訊
10617臺北市大安區羅斯福路四段1號
No.1 Sec.4, Roosevelt Rd., Taipei, Taiwan, R.O.C. 106
Tel: (02)33662353
Email: ntuetds@ntu.edu.tw
意見箱
相關連結
館藏目錄
國內圖書館整合查詢 MetaCat
臺大學術典藏 NTU Scholars
臺大圖書館數位典藏館
本站聲明
© NTU Library All Rights Reserved