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Engineering educational research section Information and Communication Technology Program |
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THI THI ZIN
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Degree 【 display / non-display 】
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Doctor of Engineering ( 2007.3 Osaka City University )
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Master of Engineering ( 2004.3 Osaka City University )
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Master of Information Science ( 1999.5 University of Computer Studies, Yangon (UCSY) )
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B.Sc (Hons) (Mathematics) ( 1995.5 University of Yangon (UY) )
Research Interests 【 display / non-display 】
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工場での作業の見える化
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高度な画像処理技術やAI技術を活用した 研究開発
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24-hour monitoring system for the elderly to support independent living
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ICT Farm Monitoring System
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Perceptual information processing
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Image Processing and Its Application
Research Areas 【 display / non-display 】
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Informatics / Perceptual information processing / Image Processing
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Informatics / Database
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Life Science / Animal production science
Papers 【 display / non-display 】
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Advanced Predictive Analytics for Fetal Heart Rate Variability Using Digital Twin Integration. Reviewed
Tunn Cho Lwin, Thi Thi Zin, Pyke Tin, E. Kino, T. Ikenoue
Sensors (Basel, Switzerland) 25 ( 5 ) 2025.2
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal)
DOI： 10.3390/s25051469
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Optimizing black cattle tracking in complex open ranch environments using YOLOv8 embedded multi-camera system. Reviewed International journal
Su Myat Noe, Thi Thi Zin, I. Kobayashi, Pyke Tin
Scientific reports 15 ( 1 ) 6820 2025.2
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Scientific Reports
Monitoring the daily activity levels of black cattle is a crucial aspect of their well-being. The rapid advancements in artificial intelligence have transformed computer vision applications, including object detection, segmentation, and tracking. This has led to more effective and precise monitoring techniques for livestock. In modern cattle farms, video monitoring is essential for analyzing behavior, evaluating health, and predicting estrus events in precision farming. This paper introduces the novel Customized Multi-Camera Multi-Cattle Tracking (MCMCT) system. This unique approach uses four cameras to overcome the challenges of detecting and tracking black cattle in complex open ranch environments. The MCMCT system enhances a tracking-by-detection model with the YOLO v8 segmentation model as the detection backbone network to develop a precision black cattle monitoring system. Single-camera setups in real-world datasets of our open ranches, covering 23.3 m x 20 m with 55 cattle, have limitations in capturing all necessary details. Therefore, a multi-camera solution provides better coverage and more accurate behavior detection of cattle. The effectiveness of the MCMCT system is demonstrated through experimental results, with the YOLOv8-MCMCT system achieving an average Multi-Object Tracking Accuracy (MOTA) of 95.61% across 10 cases of 4 cameras at a processing speed of 30 frames per second. This high accuracy is a testament to the performance of the proposed MCMCT system. Additionally, integrating the Segment Anything Model (SAM) with YOLOv8 enhances the system’s capability by automating cattle mask region extraction, reducing the need for manual labeling. Comparative analysis with state-of-the-art deep learning-based tracking methods, including Bot-sort, Byte-track, and OC-sort, further highlights the MCMCT’s performance in multi-cattle tracking within complex natural scenes. The advanced algorithms and capabilities of the MCMCT system make it a valuable tool for non-contact automatic livestock monitoring in precision cattle farming. Its adaptability ensures effective performance across varied ranch environments without extensive retraining. This research significantly contributes to livestock monitoring, offering a robust solution for tracking black cattle and enhancing overall agricultural efficiency and management.
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Enhancing Fetal Monitoring through Digital Twin Technology and Entropy-Based Fetal Heart Rate Variability Analysis Reviewed International journal
Tunn Cho Lwin, Thi Thi Zin, Pyae Phyo Kyaw, Pyke Tin, E. Kino and T. Ikenoue
International Journal of Innovative Computing, Information and Control 21 ( 1 ) 185 - 196 2025.2
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：International Journal of Innovative Computing, Information and Control
In fetal healthcare, Digital Twin Technology (DTT) offers a powerful tool for simulating fetal physiological conditions, enabling continuous, real-time monitoring and predictive analysis. This study explores the integration of DTT with entropy-based analysis of fetal heart rate variability (FHRV) to enhance fetal monitoring. Utilizing a dataset of 585 fetal electrocardiogram (ECG) recordings collected via scalp electrode monitoring during delivery, we computed entropy measures such as Markov entropy and multiscale entropy to assess fetal status. The results demonstrate that these entropy measures provide significant information regarding fetal well-being status. Moreover, the calculated entropy values correlate strongly with umbilical cord blood gas parameters. This correlation suggests that entropy-based FHRV analysis, combined with DTT, can serve as an effective and reliable method for improving the accuracy of fetal health monitoring and predicting fetal well-being as delivery approaches.
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Automated Cattle Monitoring System for Calving Time Prediction Using Trajectory Data Embedded Time Series Analysis Reviewed International journal
Wai Hnin Eaindrar Mg, Thi Thi Zin, Pyke Tin, M. Aikawa, K. Honkawa, Y. Horii
IEEE Open Journal of the Industrial Electronics Society 6 1 - 19 2025.1
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：IEEE Open Journal of the Industrial Electronics Society
This research introduces an automated system for cattle monitoring and calving time prediction, utilizing trajectory data embedded with time-series analysis. Designed for large-scale farms, our system offers continuous 12-hour monitoring, ensuring precise capture of cattle movements. By utilizing time series analysis on the trajectory data, our system predicts calving events in advance, effectively distinguishing between abnormal (requiring human assistance) and normal (not requiring assistance) for each cow. We utilized 360-degree surveillance cameras to provide comprehensive coverage without disturbing the cattle's natural behavior. We employed tailored versions of the Detectron2 and YOLOv8 models to achieve efficient and precise cattle detection, comparing their performance in terms of missed detections and false detections. For tracking, we used our Customized Tracking Algorithm (CTA), which minimizes ID switching and ensures continuous identification even in challenging conditions such as occlusions. While some ID switching errors still occur over extended tracking periods, we integrated tracking and identification to further optimize the handling of track IDs and global IDs. Our system incorporates a 4-hour forecasting of cattle movement using Euclidean Fluctuating Summation (EFS) feature combined with our custom Long Short-Term Memory (LSTM) model. Experimental results demonstrate a detection accuracy of 98.70%, tracking and identification accuracy of 99.18%, and forecasting with an average error rate of 14.07%. Furthermore, the system accurately classifies cattle as either normal or abnormal and predicts calving events a 4-hour in advance using the EFS feature, comparing its performance with various machine learning algorithms. The system's seamless integration significantly enhances farm management and animal welfare.
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Automated system for calving time prediction and cattle classification utilizing trajectory data and movement features. Reviewed International journal
Wai Hnin Eaindrar Mg, Thi Thi Zin, Pyke Tin, M. Aikawa, K. Honkawa, Y. Horii
Scientific reports 15 ( 1 ) 2378 2025.1
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Scientific reports
Accurately predicting the calving time in cattle is essential for optimizing livestock management and ensuring animal welfare. Our research focuses on developing a robust system for calving cattle classification and calving time prediction, utilizing 12-h trajectory data for 20 cattle. Our system classifies cattle as abnormal (requiring human assistance) or normal (not requiring assistance) and predicts calving times based on their individual behaviors. We employed a tailored YOLOv8 model for efficient and precise cattle detection, effectively filtering out noise such as people and trucks. Our Customized Tracking Algorithm (CTA) maintains continuous identity tracking for each cow, enabling accurate re-identification even during occlusions. To minimize some ID switching errors over extended tracking periods, we integrated IDs optimization in the CTA utilizing Global IDs identification. We extracted and compared three total movement features for classifying cattle as abnormal or normal. For predicting calving times for each cow, we utilized and compared three cumulative movement features. Our system is fully automated, detecting and tracking all 20 cattle continuously for 12 h without manual assistance, and achieving an overall accuracy of 99%. By comparing three features derived from the trajectory tracking data for each point in a frame, we achieve 100%, 95%, 85% accuracy in classifying cattle as abnormal or normal and predict their calving times with a precision of within the next 6 h, within the next 9 h, within the next 8 h, respectively. Our system enables farmers to provide timely assistance, ensuring the health and safety of both the cow and the calf. Furthermore, it aids in optimizing resource allocation and enhancing overall farm efficiency, emphasizing the critical importance of calving time prediction in sustainable livestock farming.
Books 【 display / non-display 】
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Big Data Analysis and Deep Learning Applications: Proceedings of the First International Conference on Big Data Analysis and Deep Learning (Advances in Intelligent Systems and Computing Book 744)
Thi Thi Zin (Editor), Jerry Chun-Wei Lin (Editor) （ Role： Joint editor）
Springer 2018.6
Total pages：Springer Language：English
Other Link： https://www.amazon.com/Data-Analysis-Deep-Learning-Applications-ebook/dp/B07DL46RJX
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Systems and Computing: A New Look into Web Page Ranking Systems
Thi Thi Zin, Pyke Tin, H. Hama, T. Toriu（ Role： Joint author）
Springer International Publishing 2014.10
Language：English Book type：Scholarly book
MISC 【 display / non-display 】
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Preface International coauthorship
Pan J.S., Thi Thi Zin, Sung T.W., Lin J.C.W.
Lecture Notes in Electrical Engineering 1322 LNEE v - vii 2025
Authorship：Corresponding author Language：English Publishing type：Rapid communication, short report, research note, etc. (scientific journal) Publisher：Lecture Notes in Electrical Engineering
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A Study on the Possibility of Distinguishing between Parkinson's disease and Essential Tremor using Motor Symptoms Observed by an RGB camera
Proceedings of the 34th Annual Conference of Biomedical Fuzzy Systems Association (BMFSA2022) 2022.12
Authorship：Corresponding author Language：Japanese Publishing type：Research paper, summary (national, other academic conference) Publisher：Biomedical Fuzzy Systems Association
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Tracking A Group of Black Cows Using SORT based Tracking Algorithm
Cho Cho Aye, Thi Thi Zin, M. Aikawa, I. Kobayashi
第 35 回バイオメディカル・ファジィ・システム学会年次大会 講演論文集 (BMFSA2022) 2022.12
Authorship：Corresponding author Language：English Publishing type：Research paper, summary (national, other academic conference) Publisher：バイオメディカル・ファジィ・システム学会
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Artificial Intelligence Topping on Spectral Analysis for Lameness Detection in Dairy Cattle
Thi Thi Zin, Ye Htet, San Chain Tun and Pyke Tin
第 35 回バイオメディカル・ファジィ・システム学会年次大会 講演論文集 (BMFSA2022) 2022.12
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper, summary (national, other academic conference) Publisher：バイオメディカル・ファジィ・システム学会
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Introduction to IEEE LifeTech 2022 Overview Invited International coauthorship
Thi Thi Zin and Ryota Nishimura
IEEE LifeTech2022 Abstract Book 2022.3
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper, summary (international conference) Publisher：IEEE CT Soc
Presentations 【 display / non-display 】
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Automatic Cattle Detection and Tracking for Lameness Classification Using a Single Side-View Camera International conference
Bo Bo Myint, T. Onizuka, Pyke Tin, M. Aikawa, I. Kobayashi, Thi Thi Zin
Second International Symposium on Data-Driven Intelligent Optimization for Decision Making (DIODM2024) 2024.3.26
Event date： 2024.3.24 - 2024.3.27
Language：English Presentation type：Oral presentation (general)
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画像処理技術を用いた歩行状態の数値化に関する研究
丹野 龍晟、ティティズイン
動的画像処理実利用化 ワークショップ2024 (DIA 2024) 2024.3.4
Event date： 2024.3.4 - 2024.3.5
Language：Japanese Presentation type：Poster presentation
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Kalman Velocity-based Multi-Stage Classification Approach for Recognizing Black Cow Actions International conference
Cho Cho Aye, Thi Thi Zin, M. Aikawa, I. Kobayashi
2024 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'24) 2024.2.29
Event date： 2024.2.27 - 2024.3.1
Language：English Presentation type：Oral presentation (general)
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A Novel Stochastic Model for Analysing Heart Rate Variability in the Heart-Brain Signal Communication System International conference
Thi Thi Zin, Tunn Cho Lwin, Pyke Tin
2024 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'24) 2024.2.29
Event date： 2024.2.27 - 2024.3.1
Language：English Presentation type：Oral presentation (general)
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Enhancing Precision Agriculture: Innovative Tracking Solutions for Black Cattle Monitoring International conference
Su Myat Noe, Thi Thi Zin, Pyke Tin, and Ikuo Kobayashi
2024 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'24) 2024.2.29
Event date： 2024.2.27 - 2024.3.1
Language：English Presentation type：Oral presentation (general)
Awards 【 display / non-display 】
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Student paper Award
2024.3 2024 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing Enhancing Precision Agriculture: Innovative Tracking Solutions for Black Cattle Monitoring
Su Myat Noe, Thi Thi Zin, Pyke Tin, and Ikuo Kobayashi
Award type：Award from international society, conference, symposium, etc.
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Student paper Award
2024.3 2024 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing Kalman Velocity-based Multi-Stage Classification Approach for Recognizing Black Cow Actions
Cho Cho Aye, Thi Thi Zin, M. Aikawa, I. Kobayashi
Award type：Award from international society, conference, symposium, etc.
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Best Paper Award
2023.11 The 9th International Conference on Science and Technology (ICST UGM 2023) An Innovative Framework for Cattle Activity Monitoring: Combining AI-Based Markov Chain Model with IoT Devices
Y. Hashimoto, Thi Thi Zin, Pyke Tin, I. Kobayashi and H. Hama
Award type：Award from international society, conference, symposium, etc.
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IEEE GCCE 2022 Excellent Student Paper Awards (Outstanding Prize)
2022.10 2022 IEEE 11th Global Conference on Consumer Electronics (GCCE2022) Video-Based Automatic Cattle Identification System
Su Larb Mon, Thi Thi Zin, Pyke Tin, I. Kobayashi
Award type：Award from international society, conference, symposium, etc. Country：Japan
In this paper, we propose a method to identify the cattle by using video sequences. In order to do so, we first collect 360-degree top-view video sequences to form dataset. The proposed system is composed of two parts: cattle detection and cattle identification. In the detection process, we utilize YOLOv5(You Only Look Once) model to detect the cattle region in the lane. In this stage, cattle’s location and region information are extracted and the cropped images of detected cattle regions are saved for the next stage. We then apply Convolutional Neural Network model (VGG16) to extract the features which will be used to identify individual cattle. For the classification, the proposed system used two supervised machine learning methods, Random Forest and SVM (Support Vector Machine). The accuracy of Random Forest is 98.5% and the accuracy of SVM is 99.6%. After comparing the accuracy rate of two methods, SVM get the better accuracy result. The proposed system achieved the accuracy of over 90% for both cattle detection and identification.
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Best Presentation Award
2022.9 The 16th International Conference on Innovative Computing, Information and Control (ICICIC2022) Comparative Study on Color Spaces, Distance Measures and Pretrained Deep Neural Networks for Cow Recognition
Cho Cho Mar, Thi Thi Zin, Pyke Tin, I. Kobayashi, K. Honkawa, Y. Horii
Award type：Award from international society, conference, symposium, etc. Country：Japan
Grant-in-Aid for Scientific Research 【 display / non-display 】
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画像処理技術と非接触センサを用いた牛の発情検知及び分娩監視システムの開発
Grant number：17K08066 2017.04 - 2021.03
科学研究費補助金 基盤研究(C)
Authorship：Principal investigator
畜産は全国農業総生産額の3 割以上を占める重要な産業であるが、不適切な家畜管理による生産性の低下が大きな問題となっている。その主たる原因は飼養形態の変化による1 頭あたり観察時間の短縮であり、飼養頭数の多頭化・農家の高齢化が進む畜産現場において、365 日24 時間にわたり家畜の異常や変化を観察し続けることは困難である。
申請者らは、主に非接触・非侵襲センサ情報のアルゴリズム解析技術に着目し、距離画像とビデオ画像を用いて牛の発情を検知できる独自アルゴリズムの開発に取り組んできた。本研究では、これらの技術を応用することで、牛の発情や分娩監視時の異常を自動検知できる省力的な24 時間
家畜管理システムを開発する。 -
牛の分娩監視システムに関する研究
Grant number：18J14542 2018.04 - 2020.03
科学研究費補助金 特別研究員奨励費
須見 公祐、Thi Thi Zin（受入研究者）
Authorship：Coinvestigator(s)
精度や耐久性が不十分な割に高価なウェラブル型センサの装着や、肉体的・精神的に大きな負担を強いられる目視によるカメラ映像のモニタリング等は、大規模化する畜産現場において現実的なコストで利用できるものが極めて少ない。そこで本研究では、監視カメラから得られる映像を用いて非接触型の分娩管理システムを開発することで、農家そして牛、両方の負担を減らすことを目的とする。
本来、牛は牛群と呼ばれるグループで行動を行う。そして、分娩が間近になると分娩室という分娩専用の牛舎に移される。分娩室には2 頭以上を同時に入れるケースも多く、どの牛で分娩が始まったかを識別する必要があることから、個体識別と追跡処理が必要となる。次に、分娩行動の段階を追って検知を行う。抽出する特徴としては、尻尾が上がっているかどうか、牛が立っているか座っているか、落ち着きがなくなり移動量が増加するか、子牛を出産したかどうか、親牛が子牛を舐めているかどうかなど、それぞれの過程で自動的に異常を見つけ通報を行うアルゴリズムの開発を進める。分娩行動が起きたかどうかの判断は、これらのデータから各特徴の重要度（重み）を学習させることによって行う。そして、最終目標として難産など異常行動の検知を行うために事例を蓄積しながら知識ベースを充実させ、異常事態の検知を行い、分娩の各段階を監視して異常事態の検知ならびに通報が可能なシステムの開発を目指す。 -
Development of forensic imaging modality for person identification using integration method of feature correspondences between heterogeneous images
Grant number：15K15457 2015.04 - 2018.03
Grant-in-Aid for Scientific Research Grant-in-Aid for challenging Exploratory Research
Authorship：Coinvestigator(s)
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Study of interactive teaching systems using an image processing techniques
Grant number：15K01041 2015.04 - 2018.03
Grant-in-Aid for Scientific Research Grant-in-Aid for Scientific Research(C)
Authorship：Coinvestigator(s)
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Development of automatic estrus detection systems of cattle using complementary the plane distance image and video footage
Grant number：15K14844 2015.04 - 2017.03
Grant-in-Aid for Scientific Research Grant-in-Aid for challenging Exploratory Research
Authorship：Principal investigator
Available Technology 【 display / non-display 】
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ICTを活用した牛のモニタリングシステムの開発に関する研究
安全・安心のための24時間自動見守り・監視システムの開発に関する研究
工場の作業効率化のための作業グループ検出に関する研究Home Page： 研究者データベース
Related fields where technical consultation is available：ビッグデータからの新しい知見の獲得・発見を体系的に行える数理的道具の開発
牛のモニタリング情報分析システム
高度な画像処理技術とAI活用による身体・精神機能低下患者の行動認識に関する研究
ビデオ画像を利用した新生児運動モニタリングシステムの開発に関する研究
画像処理技術を用いた疾病の特徴を示すエビを検出するシステムMessage：『画像処理を用いて様々な問題を解決すること』を目的として、農学や医学の分野に係わる学際領域の研究を幅広く行っています。多種多様な課題に対して各分野の専門家と協力して、画像処理分野からの貢献を目指して、研究・開発を行っています。