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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 資訊工程學系
Please use this identifier to cite or link to this item: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70649
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???org.dspace.app.webui.jsptag.ItemTag.dcfield???ValueLanguage
dc.contributor.advisor施吉昇(Chi-Sheng Shih)
dc.contributor.authorTzu-Yu Chungen
dc.contributor.author鐘紫育zh_TW
dc.date.accessioned2021-06-17T04:33:44Z-
dc.date.available2020-09-29
dc.date.copyright2020-09-29
dc.date.issued2020
dc.date.submitted2020-09-02
dc.identifier.citation[1] “Mathworks distortion example.” [Online]. Available: https://ww2.mathworks.cn/ help/vision/ref/cameraintrinsics.html
[2] “Grideye.” [Online]. Available: https://na.industrial.panasonic. com/products/sensors/sensors-automotive-industrial-applications/lineup/ grid-eye-infrared-array-sensor
[3] “Raspberry pi 3 model b+.” [Online]. Available: https://www.raspberrypi.org/ products/raspberry-pi-3-model-b-plus/
[4] “Arduino mini.” [Online]. Available: https://store.arduino.cc/usa/arduino-pro-mini
[5] “Infrared array sensor grid-eye (amg88) datasheet.” [Online]. Available: https: //datasheet.octopart.com/AMG8833-Panasonic-datasheet-62338626.pdf
[6] Z. Zhang, “A flexible new technique for camera calibration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330–1334, 2000.
[7] Y. Zoetgnande, J.-L. Dillenseger, G. Cormier, and A.-J. Fougères, “Robust low- resolution thermal stereo camera calibration,” 10 2018.
[8] R. Yang, W. Yang, Y. Chen, and X. Wu, “Geometric calibration of ir camera using trinocular vision,” IEEE/OSA Journal of Lightwave Technology - J LIGHTWAVE TECHNOL, vol. 29, pp. 3797–3803, 12 2011.
[9] A. Ellmauthaler, E. A. B. da Silva, C. L. Pagliari, J. N. Gois, and S. R. Neves, “A novel iterative calibration approach for thermal infrared cameras,” in 2013 IEEE International Conference on Image Processing, 2013, pp. 2182–2186.
[10] J. Heikkila and O. Silven, “A four-step camera calibration procedure with implicit image correction,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1997, pp. 1106–1112.
[11] C. Shih, J. Chou, W. Wang, and K. Huang, “Measuring gait speed using temporal and location-aware sensing services in smart homes,” in 2017 IEEE 10th Conference on Service-Oriented Computing and Applications (SOCA), 2017, pp. 173–180.
[12] “Opencv-calibratecamera-module.” [Online]. Available: https://docs.opencv. org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html# calibratecamera
[13] “Opencv-initundistortrectifymap-module.” [Online]. Available: https: //docs.opencv.org/2.4/modules/imgproc/doc/geometric_transformations.html# initundistortrectifymap
[14] “Opencv-remap-module.” [Online]. Available: https://docs.opencv.org/2.4/ modules/imgproc/doc/geometric_transformations.html#remap
[15] “Opencv-cornerharris-module.” [Online]. Available: https://docs.opencv.org/2.4/ modules/imgproc/doc/geometric_transformations.html#cornerHarris
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70649-
dc.description.abstract世界各國開始進入老齡化社會,長期照護開始成為重要的行業。在長期照護系統中,常用普通的RGB攝影機進行記錄,這會導致老年人的隱私問題。部分機構改用可以不分晝夜進行紀錄的紅外線熱感應器,但如果使用解析度較高的型號,仍然會有隱私問題。因此,我們使用超低解析度紅外線熱感應器來記錄老年人的身體狀況。
在這項研究中,我們使用了具有相機校正功能的單個超低解析度紅外線熱感應器來檢測熱源的移動距離。
一般的紅外線熱感應器是可以使用自熱式校正板來做相機校正,但是此研究中使用的紅外線熱感應器解析度遠低於一般市面上常見的機型。因此我們在固定尺寸的感測平面上記錄熱源的移動範圍,並結合熱感應器上顯示的相應位置以製作模擬校正板。通過這個模擬校正板,我們可以對超低解析度的熱影像做校正。
我們使用相機校正來獲得紅外線熱感應器的相機矩陣,用來還原感應器中的每個熱電堆感測到的實際場域大小。有了這個正確的感測場域,可以讓我們用來計算熱源的移動距離。我們的貢獻是如何製作模擬校正板,並使用校正後的圖像計算熱源的移動距離。使用我們的方法可將熱源移動的測量距離誤差降低至 6.9%。
zh_TW
dc.description.abstractThe world has begun to enter an aging society, and long-term care has become an important industry. In the long-term care system, ordinary cameras are often used for recording, causing privacy problems for the elderly. Some of them begin to use infrared sensors, which can record day and night while using a higher resolution model, and it still causes privacy problems. To solve this privacy problem, we choose thermal sensors to record the activities of the elderly.
In this study, we used a single ultra-low-resolution thermal sensor with camera calibration to detect the moving distance of the heat source. Since the resolution of our thermal sensor is too low, it is impossible to use a self-heating calibration board. Therefore, we record the moving range of the heat source on a fixed-size sensing plane and combine the corresponding position shown on the sensor to make a simulated checkerboard. We can calibrate the ultra-low-resolution thermal image through this new checkerboard.
We can use the camera calibration to get the camera matrix to restore the actual size of the sensing field in the corresponding grid of the sensor. Use this actual size, and it can help us calculate the moving distance of the heat source. Our contribution is how to make a simulated calibration board and use the corrected image to calculate the moving distance of the heat source. Using our method can reduce the error of the moving distance to 6.9\%.
en
dc.description.provenanceMade available in DSpace on 2021-06-17T04:33:44Z (GMT). No. of bitstreams: 1
U0001-2608202016411200.pdf: 10078956 bytes, checksum: e2e12e32894686ecfed40b1bfa4e1928 (MD5)
Previous issue date: 2020
en
dc.description.tableofcontents口試委員會審定書 i
Acknowledgments ii
摘要 iii
Abstract iv
1 Introduction 1
1.1 Motivation.................................. 1
1.2 Contribution................................. 2
1.3 ThesisOrganization............................. 3
2 Background and Related Work 4
2.1 Background................................. 4
2.2 RelatedWorks................................ 8
2.2.1 Robust low-resolution thermal stereo camera calibration . . . . . 8
2.2.2 Measuring Gait Speed Using Temporal and Location-Aware Sens-
ingServicesinSmartHomes.................... 9
3 System Architecture and Problem Definition 11
3.1 SystemArchitecture............................. 11
3.2 Problem Definition............................. 14
3.3 Challenges.................................. 14
4 Design and Implementation 15
4.1 ThermalImagePreprocess ......................... 15
4.2 SimulatedCheckerboard .......................... 17
4.3 CameraCalibration............................. 18
4.4 EnhancedDistanceAccuracy........................ 19
5 Performance Evaluation 21
5.1 ExperimentEnvironment .......................... 21
5.2 EvaluationResults ............................. 22
6 Conclusion 24
Bibliography 25
dc.language.isoen
dc.subject相機校正zh_TW
dc.subjectIoTzh_TW
dc.subject紅外線影像zh_TW
dc.subjectthermal imagingen
dc.subjectcamera calibrationen
dc.subjectIoTen
dc.title低解析度熱感應器之熱源定位精度與校正研究zh_TW
dc.titleCalibration of low-resolution thermal sensors and enhanced heat source positioning accuracyen
dc.typeThesis
dc.date.schoolyear108-2
dc.description.degree碩士
dc.contributor.oralexamcommittee洪士灝(Shih-Hao Hung),葉彌研(Mi-Yen Yeh)
dc.subject.keyword紅外線影像,相機校正,IoT,zh_TW
dc.subject.keywordthermal imaging,camera calibration,IoT,en
dc.relation.page26
dc.identifier.doi10.6342/NTU202004172
dc.rights.note有償授權
dc.date.accepted2020-09-02
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept資訊工程學研究所zh_TW
Appears in Collections:資訊工程學系

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