Papers - THI THI ZIN
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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.
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Utilizing Behavioral Features for Predicting Calving Time Reviewed
Wai Hnin Eaindrar Mg, Pyke Tin, M. Aikawa, I. Kobayashi, Y. Horii, K. Honkawa K., Thi Thi Zin
Lecture Notes in Electrical Engineering 1321 LNEE 148 - 159 2025
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
Accurately predicting calving time in cattle is crucial for enhancing livestock management and ensuring animal welfare. Our research presents a novel approach combining advanced computer vision and deep learning techniques to predict calving time based on cattle behavior. We employ a custom YOLOv8 model for cattle detection, achieving robust and efficient localization of individual cattle in various farm environments. Our Customized Tracking Algorithm (CTA) is utilized to maintain continuous identity tracking for each cow, allowing for precise monitoring of behavioral patterns. Feature extraction is performed using ResNet50, capturing detailed spatial characteristics of the detected cattle. These features are then organized into sequences to prepare them for temporal analysis. Finally, Custom Long Short-Term Memory (CLSTM) network is used for classification, leveraging the sequential nature of the data to predict the onset of calving with high accuracy. Our classification approach achieved an average accuracy of 94.88%. Our findings indicate promising performance from our CLSTM algorithm, accurately forecasting the remaining 3 h before calving. Through a comprehensive exploration of data collection, pre-processing, and feature engineering, our research paper establishes the foundation for training an accurate behavior model to predict calving time. Predicting calving using traditional, manual methods like observing breeding records and visual cues is complex and prone to errors, with even experts sometimes failing to make accurate predictions. Additionally, manual prediction becomes impractical and costly as farm size increases. Our automated system demonstrated a significant improvement in prediction accuracy, reducing false positives and providing timely alerts. Our proposed method demonstrates significant potential for improving the precision and reliability of calving time predictions, offering valuable insights for farm management and veterinary care.
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Cho Nilar Phyo,Thi Thi Zin, H. Hama, Pyke Tin
Lecture Notes in Electrical Engineering 1321 LNEE 54 - 62 2025
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
A Reinforcement Learning, a pivotal component of artificial intelligence, is employed by computers to learn intelligently. This study delves into the application of a significant mathematical concept, the Markov Decision Process (MDP), within the realm of Reinforcement Learning. The primary focus lies in the development of a proficient computer program designed to tackle healthcare issues. The proposed approach consists of two key elements: a unique decision-making framework and intelligent learning mechanisms. This process of combining two elements is leveraged to analyze patient information and ascertain optimal choices. Conceptualizing a patient’s healthcare journey as distinct states—such as physician visits (O), hospitalization (H), intensive care (I), or mortality (D)—the research formulates a Markov Chain Model. This model quantifies the transition probabilities between these states. Additionally, an auxiliary model is constructed to gauge the efficacy of decisions, encompassing factors like risk assessment and potential medication outcomes. The effectiveness of the proposed model, termed the Markov Model with Reinforcement Learning, is evaluated using real-world patient data from electronic health records. Encouragingly, the model demonstrates proficiency in predicting forthcoming healthcare events. This underscores its utility in prognosticating future developments within the healthcare domain.
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A Study on Health Management by Behavior Analysis of Calves Reviewed
T. Nishiyama, S. Kazuhisa, M. Aikawa, I. Kobayashi, Thi Thi Zin
Lecture Notes in Electrical Engineering 1322 LNEE 144 - 151 2025
Authorship：Last author Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
It is important to always monitor the health of cattle, especially calves, and the frequency of observation increases with environmental changes in addition to once a day. In addition, calves tend to be more susceptible to infectious diseases because of their immature immune systems. Therefore, rearing management is extremely important. And the number of dairy cattle-keeping households and the total number of cattle are decreasing, while the number of cattle per household is increasing, indicating that management is becoming larger in scale. In this study, we proposed the development of a health management system by analyzing calf behavior using a 3D camera. Experiments were conducted at the Sumiyoshi Field of Miyazaki University to confirm the effectiveness of the proposed method.
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Applying Digital Restoration Techniques in Preservation of Ancient Murals Using Diffusion-Based Inpainting Reviewed International coauthorship
Tint K.K.W., Mie Mie Tin, Thi Thi Zin, Pyke Tin
Lecture Notes in Electrical Engineering 1321 LNEE 398 - 407 2025
Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
Nowadays, with the assistance of advanced technology, everything is constantly evolving in every sector, such as transportation, economics, medication, and so on. Also, in the sector of cultural heritage, image inpainting technology has become very popular as an effective way that allows conservators to digitally restore damaged murals without physically altering the original artwork, preserving its integrity and historical authenticity. In this paper, an image inpainting framework is proposed to restore the ancient murals of Myanmar from AD 1800–1900. The framework can be subdivided into crack removal and lacuna removal. The identification of crack and lacuna damage is automatically done with segmentation and image processing methods. Crack damage is reconstructed with pixel neighboring transfer, while lacuna reconstruction is applied with coherent transport and patch-based nearest neighbor similarity color filling methods. The accuracy is tested with the damage ratio analysis, and the experimental result demonstrates that the framework can deliver satisfactory visual results in the reconstruction process of the murals.
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Automated Cattle Identification via Image-Based Ear Tag Recognition Reviewed
Y. Shimizu, Thi Thi Zin, M. Aikawa, I. Kobayashi
Lecture Notes in Electrical Engineering 1321 LNEE 138 - 147 2025
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
A cattle management system using image processing technology has been proposed to reduce the labor burden of livestock farmers and improve management efficiency. To make cattle management using image processing more efficient, individual identification is necessary. Therefore, we focused on the 10-digit individual identification number given to each cow. In this study, face detection is first performed using YOLOv8, and ear regions are set from the detected face regions. The ear tag region is extracted from the ear region using color information, and the individual identification number on the ear tag is read. The identification result of the ear tag number may not be correctly identified due to the fact that a part of the ear tag is not captured due to the movement of the face or ear. Therefore, we proposed a mechanism to give a confidence score to the character identification results, update the ear tag number identification results, and obtain the ear tag number correctly, and confirmed its effectiveness through a demonstration experiment.
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Automatic Body Temperature Detection in Calves and Alarm System Using Thermographic Camera Reviewed
Aung Si Thu Moe, Thi Thi Zin, M. Aikawa, I. Kobayashi
Lecture Notes in Electrical Engineering 1321 LNEE 190 - 198 2025
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
The health monitoring of cows is crucial in livestock farming, particularly for calves, which are more susceptible to infectious diseases than adult cattle. This vulnerability is significantly influenced by the maturation of the calf’s immune system, rearing environment, and stress management. Traditional methods of health monitoring require substantial manpower, which can be impractical and inefficient. This paper proposes an automatic body temperature detection system for calves using thermal imaging. Leveraging the capabilities of thermal images, which are widely used in security, medical, and industrial applications, this system aims to identify and monitor the body temperature of calves. By detecting the head and eyes of the calf and extracting temperature data through the Detectron2 object detection method, the system can provide timely notifications to farmers or veterinarians. The overall average detection rate of head and eye regions was 94.5%. This approach enhances the efficiency of livestock health management, reducing the reliance on manual labor and enabling early detection of health issues.
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Cattle Lameness Classification Using Cattle Back Depth Information Reviewed
San Chain Tun, Pyke Tin, M. Aikawa, I. Kobayashi, Thi Thi Zin
Lecture Notes in Electrical Engineering 1321 LNEE 160 - 170 2025
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
The livestock industry plays a crucial role in sustaining agricultural production and rural economies. Monitoring cattle health, however, presents significant challenges on large farms where traditional methods require diagnosing each animal individually. Lameness is a major issue affecting cattle health, leading to decreased production performance on many farms. Timely detection of lameness is essential for providing effective early treatment. In this study, we propose a system using specialized depth cameras to monitor and analyze cattle back information for classifying lameness scores. We employ Detectron2 for cattle detection and segmentation, and the Intersection over Union (IOU) method for tracking, focusing solely on the cattle’s depth region. We extract various features from the cattle’s back depth data and utilize three different machine learning algorithms: K-Nearest Neighbor (KNN), Gradient Boosting, and Extra Trees for lameness score classification. The models KNN, Gradient Boosting, and Extra Trees showed strong training and validation results. Testing showed Extra Trees performing well with 88.2% morning and 89.0% evening accuracy. Our approach demonstrates the potential of depth camera in effectively classifying lameness scores, offering significant implications for livestock health management. This method not only improves the efficiency and accuracy of health monitoring in large-scale farming but also provides a practical solution for early detection and treatment of lameness, thereby enhancing overall farm productivity.
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Cattle Lameness Detection Using Leg Region Keypoints from a Single RGB Camera Reviewed
Bo Bo Myint, Thi Thi Zin, M. Aikawa, I. Kobayashi, Pyke Tin
Lecture Notes in Electrical Engineering 1321 LNEE 180 - 189 2025
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
The recent rise of machine learning and deep learning has significantly impacted the field of computer vision, particularly in tasks like object detection, object tracking, and classification. This surge in interest has underscored the critical role of feature extraction as a foundational step in these machine learning pipelines. Our research focuses on applying feature extraction techniques to a cattle lameness dataset. We specifically extract features related to the movement of key points on cattle legs across a sequence of video frames. By analyzing the variations in these points, we aim to identify features that can efficiently differentiate between lame and no lame cattle using popular machine learning algorithms. All four classifiers achieved strong testing accuracy above 75%, with SVM excelling at over 84%.
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Cow’s Back Surface Segmentation of Point-Cloud Image Using PointNet++ for Individual Identification Reviewed
Pyae Phyo Kyaw, Pyke Tin, M. Aikawa, I. Kobayashi, Thi Thi Zin
Lecture Notes in Electrical Engineering 1321 LNEE 199 - 209 2025
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
An automatic cow health monitoring and management system cannot function effectively without an identification system in livestock farming. While 2D image-based computer vision currently achieves high accuracy in cow identification, its effectiveness can be significantly decreased by changes in lighting, environmental factors, and other limitations. To address these limitations, an identification system based on point-cloud images will be developed by using a combination of 3D TOF camera and 2D RGB camera. This system includes detection and segmentation stage, feature extraction stage, and identification stage. In this study, I focus on detecting and segmenting of cow back surface region from a point-cloud image using the PointNet+ + algorithm. Two segmentation models are trained and compared based on single-scale grouping (SSG) and multi-scale grouping (MSG) features. The extracted cow back surface region offers a rich set of features valuable for several applications, including individual cow identification, lameness detection, and body condition scoring.
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Evaluation of Body Condition Score for Walking Dairy Cows Using 3D Camera Reviewed
M. Chikunami, Thi Thi Zin, M. Aikawa, I. Kobayashi
Lecture Notes in Electrical Engineering 1322 LNEE 63 - 72 2025
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
Body Condition Score (BCS) is an important index for assessing body fat accumulation in cattle and plays a crucial role in managing cattle productivity, feeding efficiency, and overall health. Currently, BCS evaluations predominantly rely on visual assessment and palpation by specialized personnel, which is time-consuming and labour-intensive. Consequently, many farms refrain from utilizing BCS for cattle management. Previous studies have focused on BCS evaluation of stationary dairy cows in rotary parlours, but this approach is not feasible for small and medium-sized livestock producers lacking such facilities. To enable BCS management for dairy cows on any farm, we propose a system utilizing image processing technology for evaluating cows while walking. In this study, we capture images using three 3D cameras and construct an estimation model using feature extraction and multiple regression analysis. This model allowed the evaluation of cows with large BCS within an error margin of 0.25. Our findings suggest that this approach can significantly streamline BCS evaluation, making it accessible and practical for a broader range of dairy farms.
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From Vision to Vocabulary: A Multimodal Approach to Detect and Track Black Cattle Behaviors Reviewed
Su Myat Noe, Thi Thi Zin, Pyke Tin, I. Kobayashi
Lecture Notes in Electrical Engineering 1321 LNEE 171 - 179 2025
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
This paper investigates the potential of recent image-text foundation models for classifying black cattle mounting behavior without fine-tuning. Our approach begins with the detection and tracking of each individual black cattle using deep learning-based, fine-tuned YOLOv9 and Deep OC SORT tracking. Once completed, we employ zero-shot approaches, explicitly utilizing the multi-modal Large Language and Vision Alignment (LLAVA) and Large Language Model Meta AI (LLaMA) models. These models integrate visual and linguistic information seamlessly, enabling us to leverage pre-trained knowledge to analyze and understand black cattle behavior directly from images and accompanying text descriptions. By utilizing zero-shot learning, we can bypass the resource-intensive process of model fine-tuning, making it a highly efficient approach for behavior classification. Our approach highlights the robustness and flexibility of multimodal foundation models like LLAVA and LLaMA in handling complex tasks in the agricultural domain, demonstrating their potential for broader applications without requiring extensive retraining on specific datasets. Through our experiments, we showcase the accuracy and efficiency of this zero-shot multimodal approach, providing valuable insights into black cattle mounting behavior that can enhance livestock management and monitoring practices. We introduced a novel cattle dataset tailored for this purpose. We achieved high detection accuracy with a mAP of 0.9856% using our fine-tuned YOLOv9 model and an average tracking accuracy of 94.79% across five videos. The overall accuracy of our integrated system demonstrates its efficacy in accurately classifying and tracking cattle behaviors, underscoring the potential of zero-shot learning models in precision agriculture.
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T. Ishikawa, Thi Thi Zin, M. Aikawa, I. Kobayashi
Lecture Notes in Electrical Engineering 1322 LNEE 134 - 143 2025
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：Lecture Notes in Electrical Engineering
Rumination is a critical indicator of a cow’s physiological state, making it a valuable metric for managing cow health and predicting calving. Traditional human observation of rumination behaviour is time consuming and impractical for continuous monitoring, and sensor-based identification can be stressful for the cows due to the need for attachment. To address these challenges, this study introduces a non-contact method for identifying cow rumination. The proposed approach involves capturing video footage of multiple cows from above, recognizing cow regions, and validating the method’s effectiveness through experiments. Specifically, we utilize optical flow and frame-to-frame subtraction methods to extract moving cow regions from the recorded videos. From this data, we derive 11 features and employ a Support Vector Machine (SVM) for classification. Training the SVM with label features resulted in a test data identification accuracy of approximately 60%.
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Research on Individual Identification of Walking Cows Using a 3D Camera Reviewed
Y. Shiihara, Thi Thi Zin, M. Aikawa, I. Kobayashi
Lecture Notes in Electrical Engineering 1322 LNEE 73 - 83 2025
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Lecture Notes in Electrical Engineering
This research focuses on identifying individual cows based on features from their back regions captured by 3D cameras, aiming to enhance management efficiency and reduce labor burdens. The methodology involves using 3D cameras to collect data on cows walking through a milking parlor. The captured data is processed to extract specific features such as pixel counts and volume from the cow’s back region. Various classification methods, including SVM, k-NN, decision trees, and random forests, are employed to identify individual cows. Experimental results demonstrated that the random forest classifier achieved the highest accuracy at 95%, outperforming other methods. The study highlights the limitations of current RFID-based systems, such as cost and stress on animals, and presents a non-contact alternative that reduces labor and improves accuracy.
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AI-enhanced real-time cattle identification system through tracking across various environments Reviewed International journal
Su Larb Mon, T. Onizuka, Pyke Tin, M. Aikawa, I. Kobayashi, Thi Thi Zin
Scientific Reports 14 ( 1 ) 17779 2024.12
Authorship：Lead author, Last author Language：English Publishing type：Research paper (scientific journal) Publisher：Scientific Reports
Video-based monitoring is essential nowadays in cattle farm management systems for automated evaluation of cow health, encompassing body condition scores, lameness detection, calving events, and other factors. In order to efficiently monitor the well-being of each individual animal, it is vital to automatically identify them in real time. Although there are various techniques available for cattle identification, a significant number of them depend on radio frequency or visible ear tags, which are prone to being lost or damaged. This can result in financial difficulties for farmers. Therefore, this paper presents a novel method for tracking and identifying the cattle with an RGB image-based camera. As a first step, to detect the cattle in the video, we employ the YOLOv8 (You Only Look Once) model. The sample data contains the raw video that was recorded with the cameras that were installed at above from the designated lane used by cattle after the milk production process and above from the rotating milking parlor. As a second step, the detected cattle are continuously tracked and assigned unique local IDs. The tracked images of each individual cattle are then stored in individual folders according to their respective IDs, facilitating the identification process. The images of each folder will be the features which are extracted using a feature extractor called VGG (Visual Geometry Group). After feature extraction task, as a final step, the SVM (Support Vector Machine) identifier for cattle identification will be used to get the identified ID of the cattle. The final ID of a cattle is determined based on the maximum identified output ID from the tracked images of that particular animal. The outcomes of this paper will act as proof of the concept for the use of combining VGG features with SVM is an effective and promising approach for an automatic cattle identification system
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ENHANCING FACTORY EFFICIENCY: DATA-DRIVEN ANALYSIS WITH WORKER DETECTION AND TRACKING SYSTEM Reviewed International journal
I. Hidaka, S. Inoue, T. Ishikawa, H. Tamura, Thi Thi Zin
ICIC Express Letters 18 ( 12 ) 1327 - 1337 2024.12
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters
As Japan’s population continues to decline, one challenge for small and medium-sized enterprises (SMEs) is the decline in productivity due to a shortage of employees. As a result, many small- and medium-scale factories are introducing AI and IoT to automate their operations so that they can handle them with fewer employees. However, some factories are not automating because it is more economical for employees to do the work directly. Such companies can produce more economic benefits than they do now with less labor by eliminating waste from their current work and improving work efficiency. Therefore, in this paper, we propose a system to detect and track workers in a factory using a 4K camera to obtain trajectories (lines of movement) of each work group and acquire data to be used for improving work efficiency. To determine the work group, markers are attached to the top of workers’ helmets. The proposed system is simulated with a dataset taken by us in a real factory, and the average accuracy of helmet detection and tracking is 92.9% and 86.0%, respectively. The proposed system allows us to visually see the trajectory of workers, which will lead to decision making on staffing and work process changes and improve work efficiency.