Papers - THI THI ZIN
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Robust tracking of cattle using super pixels and local graph cut for monitoring systems Reviewed International journal
Y. Hashimoto, H. Hama, Thi Thi Zin
International Journal of Innovative Computing, Information and Control 16 ( 4 ) 1469 - 1475 2020.8
Authorship：Last author Language：English Publishing type：Research paper (scientific journal) Publisher：International Journal of Innovative Computing, Information and Control
This paper proposes a robust tracking method of Japanese black cattle. Development of a cattle monitoring system using non-contact and non-invasive methods to improve productivity is a strong demand from livestock farmers in aged society. As one of elemental technologies to realize it, we focus on tracking of cattle for detecting estrus behaviors using video camera. The conventional methods like inter-frame difference and background subtraction do not work well under supposed environment. So we propose a new updating method of ROI (Region of Interest) and Scribbles (for foreground and background) according to the movement of the centroid of the extracted cattle region. SP (Super Pixel) and LGC (Local Graph Cut) are adopted for robust cattle region extraction. The tracking without updating soon fails before cattle goes out of frame, but the tracking with the proposed updating has been successfully continued until cattle has gone out. Through the experimental results carried at Sumiyoshi Field attached to Miyazaki University, the effectiveness of the proposed method has been confirmed.
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Body Condition Score Estimation Based on Regression Analysis Using a 3D Camera Reviewed
Thi Thi Zin, Pann Thinzar Seint, Pyke Tin, Yoichiro Horii and Ikuo Kobayashi
sensors 20 ( 13 ) 2020.7
Language：English Publishing type：Research paper (scientific journal)
The Body Condition Score (BCS) for cows indicates their energy reserves, the scoring for which ranges from very thin to overweight. These measurements are especially useful during calving, as well as early lactation. Achieving a correct BCS helps avoid calving difficulties, losses and other health problems. Although BCS can be rated by experts, it is time-consuming and often inconsistent when performed by different experts. Therefore, the aim of our system is to develop a computerized system to reduce inconsistencies and to provide a time-saving solution. In our proposed system, the automatic body condition scoring system is introduced by using a 3D camera, image processing techniques and regression models. The experimental data were collected on a rotary parlor milking station on a large-scale dairy farm in Japan. The system includes an application platform for automatic image selection as a primary step, which was developed for smart monitoring of individual cows on large-scale farms. Moreover, two analytical models are proposed in two regions of interest (ROI) by extracting 3D surface roughness parameters. By applying the extracted parameters in mathematical equations, the BCS is automatically evaluated based on measurements of model accuracy, with one of the two models achieving a mean absolute percentage error (MAPE) of 3.9%, and a mean absolute error (MAE) of 0.13.
DOI： 10.3390/s20133705
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Automatic cow location tracking system using ear tag visual analysis Reviewed International journal
Thi Thi Zin, Moe Zet Pwint, Pann Thinzar Seint, Shin Thant, S. Misawa, K. Sumi, K. Yoshida
Sensors (Switzerland) 20 ( 12 ) 1 - 18 2020.6
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Sensors (Switzerland)
Nowadays, for numerous reasons, smart farming systems focus on the use of image processing technologies and 5G communications. In this paper, we propose a tracking system for individual cows using an ear tag visual analysis. By using ear tags, the farmers can track specific data for individual cows such as body condition score, genetic abnormalities, etc. Specifically, a four-digit identification number is used, so that a farm can accommodate up to 9999 cows. In our proposed system, we develop an individual cow tracker to provide effective management with real-time upgrading enforcement. For this purpose, head detection is first carried out to determine the cow’s position in its related camera view. The head detection process incorporates an object detector called You Only Look Once (YOLO) and is then followed by ear tag detection. The steps involved in ear tag recognition are (1) finding the four-digit area, (2) digit segmentation using an image processing technique, and (3) ear tag recognition using a convolutional neural network (CNN) classifier. Finally, a location searching system for an individual cow is established by entering the ID numbers through the application’s user interface. The proposed searching system was confirmed by performing real-time experiments at a feeding station on a farm at Hokkaido prefecture, Japan. In combination with our decision-making process, the proposed system achieved an accuracy of 100% for head detection, and 92.5% for ear tag digit recognition. The results of using our system are very promising in terms of effectiveness.
DOI： 10.3390/s20123564
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Body Condition Score Assessment of Depth Image using Artificial Neural Network Reviewed International journal
Thi Thi Zin, Pann Thinzar Seint, Pyke Tin, Y. Horii
ACM International Conference Proceeding Series: ICCMS '20: Proceedings of the 12th International Conference on Computer Modeling and Simulation 33 - 37 2020.6
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：ACM International Conference Proceeding Series
Body Condition Score (BCS) is a visual sign for managing feeding program. It can be described by numeric numbers to estimate available fat reserves and it also allows producers to make better management decisions. Ignoring BCS until it becomes too thin or fat may result in production and animal losses economically. In this paper, we proposed the BCS assessment tool by using depth information from 3D camera. The experimental data were collected in Oita prefecture and BCS scores were taken under the guidance of experts. The information of depth images are used as feature vectors to the input of Artificial Neural Network (ANN) classifier. The proposed system has achieved the good results for classifying two groups of BCS (3.5 and 3.75) with the overall accuracy of 86%.
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Time to Dairy Cow Calving Event Prediction by Using Time Series Analysis Reviewed International journal
Thi Thi Zin, K. Sumi, Pyke Tin
ACM International Conference Proceeding Series: ICCMS '20: Proceedings of the 12th International Conference on Computer Modeling and Simulation 143 - 146 2020.6
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：ACM International Conference Proceeding Series
In these days the precision dairy farming which is utilization of the Information and Communication Technologies (ICT) has become one of front line research topics in dairy science as well as in data science leading to Agriculture 4.0. An increase in on-farm mortality due to the occurrence calving difficulties with late assistance can cause possible problems not only for animal welfare but also economic losses to the farmers. In this aspect, calving is an extremely important event in the life of a dairy cow. On the other hand, the time around calving is also a critical period since clinical disorders, and calving problems can occur. Calving difficulties are also becoming increasingly common with many dairy cows requiring assistance at the time of calving. To maximize welfare and minimize losses due to calving difficulties, all animals need to be individually monitored to identify any calving difficulties or health problems as early as possible. In addition, it is important to know the exact time of calving event occur so that timely assistance can be made. In this paper, we propose a continuous video monitoring system for time-to calving event investigation based on time series analysis to achieve an accurate calving time prediction. In doing so we have employed three time series models of autoregressive, moving average smoothing. At the same time, we have confirmed the validity of the proposed method by using the real life data experimented on the University Dairy Farm and one of large dairy farms in Japan. The experimental results show that the proposed time series method is promising and can lead to a new prospect in modern precision dairy farming.
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Image processing technique and hidden markov model for an elderly care monitoring system Reviewed International journal
Swe Nwe Nwe Htun, Thi Thi Zin, Pyke Tin
Journal of Imaging 6 ( 6 ) 49 2020.6
Language：English Publishing type：Research paper (scientific journal) Publisher：Journal of Imaging
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Advances in image processing technologies have provided more precise views in medical and health care management systems. Among many other topics, this paper focuses on several aspects of video-based monitoring systems for elderly people living independently. Major concerns are patients with chronic diseases and adults with a decline in physical fitness, as well as falling among elderly people, which is a source of life-threatening injuries and a leading cause of death. Therefore, in this paper, we propose a video-vision-based monitoring system using image processing technology and a Hidden Markov Model for differentiating falls from normal states for people. Specifically, the proposed system is composed of four modules: (1) object detection; (2) feature extraction; (3) analysis for differentiating normal states from falls; and (4) a decision-making process using a Hidden Markov Model for sequential states of abnormal and normal. In the object detection module, background and foreground segmentation is performed by applying the Mixture of Gaussians model, and graph cut is applied for foreground refinement. In the feature extraction module, the postures and positions of detected objects are estimated by applying the hybrid features of the virtual grounding point, inclusive of its related area and the aspect ratio of the object. In the analysis module, for differentiating normal, abnormal, or falling states, statistical computations called the moving average and modified difference are conducted, both of which are employed to estimate the points and periods of falls. Then, the local maximum or local minimum and the half width value are determined in the observed modified difference to more precisely estimate the period of a falling state. Finally, the decision-making process is conducted by developing a Hidden Markov Model. The experimental results used the Le2i fall detection dataset, and showed that our proposed system is robust and reliable and has a high detection rate.
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Usability of Tablet Mobile Devices for Offline Handwritten Character Recognition Reviewed
Thi Thi Zin, T. Otsuzuki
ICIC Express Letters, Part B: Applications 11 ( 6 ) 587 - 593 2020.6
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters, Part B: Applications
In recent years, offline handwritten character recognition has been one of the major topics and challenging research areas in the field of pattern recognition and image processing. Moreover, it is a very problematic research area due to the nature of hand writing styles which can vary from one user to another. On top of that, writings by early learners in formal and non-formal education make the problem more complex and challenging. Generally speaking, the early school age children have diversity in handwriting style, variation in angle, size and shape of characters, making the problems of character recognition more difficult. The number of students has been increasing year by year worldwide. However, due to lack of teachers, many children cannot access high quality education. Therefore, this paper proposes offline character recognition for handwritten characters written on tablet mobile devices. This paper can be considered as an additional supporting system for education of preschool children. This proposed method firstly performs character segmentation process on words acquired from the tablet. In the feature extraction process Histogram of Oriented Gradients (HOG) and Bag of Visual Words (BOVW) are used. Support Vector Machine (SVM) is applied in classification process. Some experimental results are shown to confirm the proposed method.
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Virtual grounding point concept for detecting abnormal and normal events in home care monitoring systems Reviewed International journal
Swe Nwe Nwe Htun, Thi Thi Zin, H. Hama
Applied Sciences (Switzerland) 10 ( 9 ) 3005 2020.5
Language：English Publishing type：Research paper (scientific journal) Publisher：Applied Sciences (Switzerland)
In this paper, an innovative home care video monitoring system for detecting abnormal and normal events is proposed by introducing a virtual grounding point (VGP) concept. To be specific, the proposed system is composed of four main image processing components: (1) visual object detection, (2) feature extraction, (3) abnormal and normal event analysis, and (4) the decision-making process. In the object detection component, background subtraction is first achieved using a specific mixture of Gaussians (MoG) to model the foreground in the form of a low-rank matrix factorization. Then, a theory of graph cut is applied to refine the foreground. In the feature extraction component, the position and posture of the detected person is estimated by using a combination of the virtual grounding point, along with its related centroid, area, and aspect ratios. In analyzing the abnormal and normal events, the moving averages (MA) for the extracted features are calculated. After that, a new curve analysis is computed, specifically using the modified difference (MD). The local maximum (lmax), local minimum (lmin), and half width value (vhw) are determined on the observed curve of the modified difference. In the decision-making component, the support vector machine (SVM) method is applied to detect abnormal and normal events. In addition, a new concept called period detection (PD) is proposed to robustly detect the abnormal events. The experimental results were obtained using the Le2i fall detection dataset to confirm the reliability of the proposed method, and that it achieved a high detection rate.
DOI： 10.3390/app10093005
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Motion Detection Method for Reducing Foreground Aperture Problem in Background Modelling Reviewed
Thi Thi Zin, Pyke Tin, Cho Nilar Phyo
LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies 260 - 261 2020.3
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies
Motion detection plays as an important role in the implementation of video surveillance system and video analysis applications. The detection of moving foreground objects from the complex background scene is the first step in most of the computer vision based surveillance applications. In this paper, we present a new motion detection method using background modelling technique and moving average feature. For establishing the robust background model, we create the Gamma Mixture Model based on the Gamma distribution function. For handling the foreground aperture problem, we use the feature called moving average which can well recognize the alive silent objects. As post-processing, we handle the shadow removing process by generating the dynamic shadow threshold. The experimental results show that the proposed motion detection method can detect the accurate foreground object even though the foreground object appears as silent background objects in real-time environment.
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Handwritten Characters Segmentation using Projection Approach Reviewed
Thi Thi Zin, Shin Thant, Ye Htet, Pyke Tin
LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies 107 - 108 2020.3
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies
In the area of optical character recognition, handwritten character segmentation is still an ongoing process. Having good segmentation result can provide the better recognition accuracy. In the proposed system, segmentation is carried out mainly on labelling and projection concepts. The input word is firstly labelled. Then, the modified word is segmented with projection approach. The experiments are performed on local dataset with 1600 words approximately and the system gets segmentation accuracy around 85.75 percentage.
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Cow Identification System using Ear Tag Recognition Reviewed
Thi Thi Zin, S. Misawa, Moe Zet Pwint, Shin Thant, Pann Thinzar Seint, K. Sumi, K. Yoshida
LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies 65 - 66 2020.3
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies
In precision dairy farming, the valid record of individual cow identification is an important factor in large herds management. In this paper, we propose a cow's ear tag recognition system that can be used in dairy cow management. Firstly, cow's head detection is performed by using You Only Look Once (YOLO) object detector followed by ear tag recognition. The ear tag extraction and recognition processes are carried out by image processing techniques and Convolutional Neural Network (CNN) classifier on detected cow's head images. The experiments are conducted by using videos from dairy farm at Hokkaido prefecture, Japan. The proposed system achieved the reliable results which will support to give the informative status in smart farming.
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Human Action Analysis Using Virtual Grounding Point and Motion History Reviewed
Swe Nwe Nwe Htun, Thi Thi Zin, H. Hama
LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies 249 - 250 2020.3
Language：English Publishing type：Research paper (international conference proceedings) Publisher：LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies
In this paper, we propose an approach to human action analysis for home care monitoring system in the aspect of image processing and life science technologies. We introduce a new concept of a virtual grounding point representing the position of a target person as an innovated feature for action analysis. Specifically, in developing action analysis, the background subtraction is firstly conducted by applying the Mixture of Gaussian and low rank subspace learning. After that, the graph cut is embedded to enhance the foregrounds in order to detect both of moving and motionless object. Secondly, the virtual grounding point is calculated by using the centroid of silhouette image. Finally, motion of the person is estimated by using timed motion history image in order to improve the accuracy of action analysis. A series of the experiments are conducted to confirm the effectiveness of the proposed method.
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The Body Condition Score Indicators for Dairy Cows Using 3D Camera Reviewed
Thi Thi Zin, Pann Thinzar Seint, Pyke Tin, Y. Hori
International Workshop on Frontiers of Computer Vision (IW-FCV) 1 - 8 2020.2
Language：English Publishing type：Research paper (international conference proceedings)
Body Condition Score (BCS) evaluates energy reserves of cows when making nutritional management. This information is also useful in fertility rate and milk production. Keeping cows in appropriate condition throughout the production cycle can improve reproductive efficiency and positive impact for economics. In this paper, we propose the effective indicators for automated BCS measurement system and the experimental data are collected at the milking station. We compute the variation of angular area from automatic anatomical point extraction and learning parameters on Region of Interest (ROI) from 3D information. The statistical analysis of BCS trend on year-round data are presented to give the detailed illustrations for dairy farmers. According to the recorded BCS trends, the proposed method can validate that the lower BCS were obtained in early stage of lactation period rather than other seasons.
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Some Aspects of Mathematical Modeling Techniques in Dairy Science Reviewed
Thi Thi Zin, Pyke Tin and H. Hama
International Workshop on Frontiers of Computer Vision (IW-FCV) 1 - 8 2020.2
Language：English Publishing type：Research paper (international conference proceedings)
Today world is floating in the ocean of data science which is a multi-disciplinary approach to every aspect of our better life. Agriculture 4.0 is coming in and Industry 4.0 is moving forward so that second Information and Communication revolutionary becomes to play a key role in this era. On the other hands, consumers are demanding more quality dairy products than before. Dairy farmers are eager to use the information and communication technologies that push the precision dairy farming to frontier innovations. Due to a shortage of labor in dairy farms and at the same time the farm sizes are bigger, the utilization of ICT along with artificial intelligence and the internet of things are becoming appropriate technologies. Whatever it may be, fundamentally we need to do some preliminary research prior to large scale and wide applications to the real world. In almost analytical and empirical research, we are trying in the ocean of data science. These data could not be used efficiently unless we do further processing and projections. The raw data need to be processed to obtain useful information and knowledge, which could be used for important on-farms decisions. This kind of process is called mathematical modeling which could support to decision-makers to produce correct and valid decisions. In this paper, we will present and analyze some mathematical models in the frame works of dairy science. Moreover, we shall illustrate how these models could be used in practical ways.
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Background Modelling Using Temporal Average Filter and Running Gaussian Average Reviewed
Thi Thi Zin, Cho Cho Mar and K. Sumi
International Workshop on Frontiers of Computer Vision (IW-FCV) 1 - 8 2020.2
Language：English Publishing type：Research paper (international conference proceedings)
Object detection is very important and fundamental stage for further post processing stages such as individual identification, action recognition, tracking and behavior analysis. Traditional object detection methods start from back-ground modelling stage. Background modelling is a complex and challenging task which highly depends on the speed of moving objects (foreground objects) and stability of static regions (background region). In this paper, we proposed pixel level background modelling method. In this background modelling task, the two main components of background modelling are proposed; (1) background model initialization by selecting the dynamic frames from video sequence and building the model with Temporal Average filter and (2) updating the back-ground model by using Running Gaussian Average method. These methods are applied to RGB images.
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A Hybrid Visual Stochastic Approach to Dairy Cow Monitoring System Reviewed
Thi Thi Zin, Pyke Tin, I. Kobayashi, H Hama
Transactions on Engineering Technologies 119 - 129 2020.1
Language：English Publishing type：Research paper (scientific journal)
In the era of fourth industrial revolution or Industry 4.0 together with the second information and communication technology era, the way we human beings live, act and do are always challenging by the waves of new technologies such as Internet of Things, Artificial Intelligence, Cloud Computing so on and so forth. These new technologies also drive life science, academic, business and production industries for better sides. Among them, one of challenging and innovative technology in life science industry is precision dairy farming which has been pushed into frontline topic among the academia and industry farm managers. Generally speaking, the precision dairy farming can be defined as the use of technologies advanced or simple to analyze the physical and mental behaviors of an individual cows for specifying health and profitability indicators so that overall management and farm performance are to be improved. In other words, the precision dairy farming will focus on welfare and health care for making the farm returns optimal through the use of technologies. Here, technologies may range from daily milk recording to automatic body conditions scoring for individual cows and an accurate prediction of calving time and reporting unusual occurrences during the delivery times.
In order to realize the objectives of precision dairy farming as a fundamental step, a hybrid visual stochastic approach which is a fusion of image technology and statistical method to dairy cow monitoring system is introduced in this paper. The proposed system will investigate four key areas for the precision dairy farming namely: cow identification, body condition scoring, detection of estrus behavior, prediction of time for the occurrence calving event. In doing so, the combination of image technology, statistical methods and stochastic models will be utilized. Specifically, image processing methods will be performed to detect cow activities such as standing, lying and walking in association with time, space and frequencies. Then collected data are to be transformed into a stochastic model of special type of Markov Chain for decision making process. Then some experimental results will be shown by using both image and statistical data collected from the real life environments and some available datasets. -
Robust tracking of cattle using super pixels and local graph cut for monitoring systems Reviewed
Yukie Hashimoto, Hiromitsu Hama, ThiThi Zin
International Journal of Innovative Computing, Information and Control ６（４） 1469 - 1475 2020
Authorship：Last author Language：English Publishing type：Research paper (scientific journal)
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A Correlated Random Walk Modeling Method for Dairy Cow Inter-calving Body Condition Score Pattern Analysis Reviewed
ThiThi Zin, Pyke Tin
ICIC Express Letters, 14 2020
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (scientific journal)
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Robust Tracking of Cattle Using Super Pixels and Local Graph Cut for Monitoring Systems Reviewed
ThiThi Zin
International Journal of Innovative Computing, Information and Control (IJICIC) 16 2020
Publishing type：Research paper (scientific journal)
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Motion History and Shape Orientation Based Human Action Analysis Reviewed
Swe Nwe Nwe Htun, Thi Thi Zin
2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019 754 - 755 2019.10
Authorship：Corresponding author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019
In recent decades, many research works focused on the considerations of falling and post-falling event analysis in the aspect of image processing technology to meet the consumer perspective of users. In this paper, the more informative considerations of human action analysis are developed for the prior state of fall or normal actions. In doing so, the effective background subtraction namely the Mixture of Gaussian with Low-Rank Matrix Factorization model is used to obtain robust and adaptive foreground from the practical video sequences. Then, the spatial-temporal bilateral grid for video sequences is constructed by using a standard graph cut theory to improve the cleaned foreground in order to detect the moving/motionless object region. Then, human actions are analyzed by employing motion history and shape orientation using the approximated ellipse method. The experiments are conducted on publicly available video sequences and our simulated video sequences.