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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 資訊工程學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92660
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor傅楸善zh_TW
dc.contributor.advisorChiou-Shann Fuhen
dc.contributor.author林佳城zh_TW
dc.contributor.authorJia-Cheng Linen
dc.date.accessioned2024-05-31T16:05:45Z-
dc.date.available2024-06-01-
dc.date.copyright2024-05-31-
dc.date.issued2023-
dc.date.submitted2024-05-28-
dc.identifier.citation[1] T. Liu, Z. Zhu, H. Guan, “Research on Some Key Issues Based on 1R1V Sensing Information Fusion,” Automotive Component, Vol. 22, pp. 22–25, 2021.
[2] Z. G. Ma and Y. Zheng, “A Sensor-Based Target Track Initiation Decision Algorithm for Camera and Millimeter Wave Radar Fusion Systems,” Shanghai Automotive, Vol. 17, pp. 4–8, 2020.
[3] Z. Yong, Y, Dong, F. Hou, and J. Wu, "Review on Millimeter-Wave Radar and Camera Fusion Technology," Sustainability, Vol. 14, No. 9, 5114, pp. 1-32, https://doi.org/10.3390/su14095114, 2022.
[4] 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,” https://arxiv.org/abs/2110.06864, 2022.
[5] Z. Wei, F. Zhang, S. Chang, Y. Liu, H. Wu, and Z. Feng, “mmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review,” Sensors, Vol. 22, No. 7, 2542, https://doi.org/10.3390/s22072542, 2022.
[6] Y. Wang, M. Wen, C. Zhang, and J. Lin, "RVNet: A Fast and High Energy Efficiency Network Packet Processing System on RISC-V," Proceedings of IEEE International Conference on Application-specific Systems, Architectures and Processors, Seattle, WA, pp. 107-110, doi: 10.1109/ASAP.2017.7995266, 2017.
[7] F. Nobis, M. Geisslinger, M. Weber, J. Betz, and M. Lienkamp, "A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection," Proceedings of Sensor Data Fusion: Trends, Solutions, Applications (SDF), Bonn, Germany, pp. 1-7, doi: 10.1109/SDF.2019.8916629, 2019.
[8] X. Guo, J. Du, J. Gao, and W. Wang, “Pedestrian Detection Based on Fusion of Millimeter Wave Radar and Vision,” Proceedings of International Conference on Artificial Intelligence and Pattern Recognition, Beijing, China, pp. 38–42, 2018.
[9] Z. Zhang, “A Flexible New Technique for Camera Calibration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330-1334, doi: 10.1109/34.888718, 2000.
[10] T. Wang, N. Zheng, J. Xin, and Z. Ma, “Integrating Millimeter Wave Radar with a Monocular Vision Sensor for On-Road Obstacle Detection Applications,” Sensors, No. 9, pp. 8992-9008, https://doi.org/10.3390/s110908992, 2011.
[11] H.W. Kuhn, “The Hungarian Method for the Assignment Problem,” Naval Research Logistics (NRL), Vol. 52, No. 1, https://doi.org/10.1002/nav.3800020109, 1955.
[12] R. E. Kalman, “A New Approach to Linear Filtering and Prediction Problems,” Transactions of the ASME--Journal of Basic Engineering, Vol. 82, No. Series D, pp. 35-45, https://doi.org/10.1115/1.3662552, 1960.
[13] A. Bewley, Z. Ge, L. Ott, F. Ramos and B. Upcroft, "Simple Online and Realtime Tracking," 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 2016, pp. 3464-3468, doi: 10.1109/ICIP.2016.7533003.
[14] Z. Ge, S. Liu, F. Wang, Z. Li and J. Sun, “YOLOX: Exceeding YOLO Series in 2021,” ArXiv, Vol. abs/2107.08430, https://doi.org/10.48550/arXiv.2107.08430, 2021.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92660-
dc.description.abstract本論文提出林融合:一個用於室內人員追蹤的毫米波雷達與攝影機融合系統。毫米波雷達負責感知行走的人,可以同時對多個目標進行測距、測速以及方位角測量,從而得出人在空間中的具體位置與速度。同時,攝影機端利用深度學習中的物件偵測和追蹤的方法,獲得人在影像中的位置與唯一標籤。將這兩個裝置得到的資訊進行融合,可以得到在空間中每個人的唯一識別符號和具體位置。
為了驗證系統的有效性,我們設計了一系列實驗。在室內環境中收集了毫米波雷達和攝影機的資料,並使用我們的融合系統對這些資料進行處理。我們分別對坐標校正方法和位置估計方法進行了實驗,以確保融合精度。我們的系統性能評估指標是觀察每個人的唯一識別符號是否發生變化。
本研究提出了一種基於毫米波雷達和攝影機融合的人物追蹤系統,並通過一系列實驗對其精密度和性能進行了評估,證明了系統在不同情況下的穩定性和可靠性。該系統能夠有效地對空間中的人物進行追蹤與定位。
zh_TW
dc.description.abstractIn this thesis, we propose LinFusion: a fusion system for indoor human tracking, which integrates millimeter-wave radar and color camera. The millimeter-wave radar is responsible for sensing walking individuals, and can simultaneously measure distance, velocity, and azimuth of multiple targets, obtaining the specific position and speed of people in space. Meanwhile, the color camera side uses object detection and tracking methods in deep learning to obtain the position and unique identifier of people in the image. By fusing the information obtained from these two sensors, the unique identifier and specific position of each person in space can be obtained.
To verify the effectiveness of the system, a series of experiments are designed. Millimeter-wave radar and color camera data are collected in an indoor environment, and our fusion system is used to process the data. We conduct experiments on our coordinate calibration method and position estimation method to ensure fusion accuracy. The system's performance evaluation metric is whether the unique identifier of each person changes.
We propose a human tracking system based on millimeter-wave radar and color camera fusion, and evaluates its accuracy and performance through a series of experiments, demonstrating the stability and reliability of the system under different situations. The system can effectively track and locate people in space.
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dc.description.provenanceMade available in DSpace on 2024-05-31T16:05:45Z (GMT). No. of bitstreams: 0en
dc.description.tableofcontents誌謝 i
中文摘要 ii
ABSTRACT iii
CONTENTS v
LIST OF FIGURES viii
LIST OF TABLES xi
Chapter 1 Introduction 1
1.1 Overview 1
1.2 Millimeter-Wave Radar 2
1.3 Color Camera 3
1.4 Sensor Fusion 5
1.5 Thesis Organization 8
Chapter 2 Related Works 9
2.1 Multiple-Object Detection and Tracking 9
2.1.1 Kalman Filter 9
2.1.2 Hungarian Algorithm 10
2.1.3 SORT 11
2.1.4 ByteTrack 12
2.2 Calibration between mmWave Radar and Color Camera 13
2.2.1 Space Synchronization (Coordinate Calibration) 15
2.2.2 Time Synchronization 18
Chapter 3 Methodology 21
3.1 Overview 21
3.2 Multi-Object Tracking 22
3.3 Calibration between mmWave Radar and Color Camera 23
3.3.1 Time Synchronization 23
3.3.2 Space Synchronization / Coordinate Calibration 25
3.4 Position Estimation 27
3.5 Intelligent Dataset Collection 29
3.6 Target Fusion/Matching 30
3.7 UID Assignment Strategy 33
Chapter 4 Experimental Results 38
4.1 Environment 38
4.2 Dataset 42
4.2.1 Training Dataset 42
4.2.2 Testing Dataset 44
4.3 Space Synchronization / Coordinate Calibration 45
4.4 Position Estimation 50
4.5 UID Assignment 54
Chapter 5 Conclusion and Future Works 58
References 60
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dc.language.isoen-
dc.subject林融合zh_TW
dc.subject深度學習zh_TW
dc.subject資訊融合zh_TW
dc.subject視覺zh_TW
dc.subject毫米波雷達zh_TW
dc.subjectMillimeter-wave Radaren
dc.subjectData Fusionen
dc.subjectLinFusionen
dc.subjectDeep Learningen
dc.subjectVisionen
dc.title林融合:毫米波雷達與彩色相機融合用於人物追踪zh_TW
dc.titleLinFusion: Millimeter Wave Radar and Color Camera Fusion for People Trackingen
dc.typeThesis-
dc.date.schoolyear112-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee王獻章;方瓊瑤zh_TW
dc.contributor.oralexamcommitteeHsien-Chang Wang;Chiung-Yao Fangen
dc.subject.keyword林融合,毫米波雷達,視覺,資訊融合,深度學習,zh_TW
dc.subject.keywordLinFusion,Millimeter-wave Radar,Vision,Data Fusion,Deep Learning,en
dc.relation.page62-
dc.identifier.doi10.6342/NTU202401018-
dc.rights.note同意授權(限校園內公開)-
dc.date.accepted2024-05-28-
dc.contributor.author-college電機資訊學院-
dc.contributor.author-dept資訊工程學系-
dc.date.embargo-lift2029-05-26-
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