Presentations -
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Assessing Dairy Cow Lameness using Principal Component Analysis on 3D Images Invited International conference
Pyke Tin and Thi Thi Zin
The Second Sakura Workshop for Consumer Electronics, Computer, Communication, and Information Technologies (Taichaung) 2023.3.27
Event date： 2023.3.27 - 2023.3.29
Language：English Presentation type：Symposium, workshop panel (public)
Venue：Taichaung Country：Taiwan, Province of China
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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
The 35th Annual Conference of Biomedical Fuzzy Systems Association (BMFSA2022) 2022.12.17
Event date： 2022.12.17 - 2022.12.18
Language：Japanese Presentation type：Oral presentation (general)
Movements that appear unconsciously are called involuntary movements. Especially, rhythmic involuntary movements are called tremors. Tremor interferes with daily life and is one of the characteristic symptoms of neurological disorders such as Parkinson's disease (PD) and essential tremor (ET). Symptoms and progression of PD patients are assessed by a neurologist through neurological examination and interview according to the revised Unified Parkinson's Disease Rating Scale (UPDRS) sponsored by the Movement Disorders Society (MDS). PD is associated with other symptoms such as bradykinesia, whereas ET is characterized by tremor alone. However, in the early onset of PD, other symptoms may not be noticeable, and even neurologists may find it difficult to differentiate it from ET. In this paper, to solve this issue of getting lost in this judgment, consider whether it is possible to distinguish between PD and ET two types of neurological disorders from two approaches, time domain and frequency domain which from observed tremor movement by using an RGB camera.
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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
バイオメディカル・ファジィ・システム学会 第 35 回年次大会 2022.12.17
Event date： 2022.12.17 - 2022.12.18
Language：English Presentation type：Oral presentation (general)
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Artificial Intelligence Topping on Spectral Analysis or Lameness Detection in Dairy Cattle
Thi Thi Zin, Ye Htet, San Chain Tun and Pyke Tin
バイオメディカル・ファジィ・システム学会 第 35 回年次大会 2022.12.17
Event date： 2022.12.17 - 2022.12.18
Language：English Presentation type：Oral presentation (general)
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Cattle Lameness Detection using Advanced Digitization Technologies Invited International conference
Thi Thi Zin, T. Onizuka, San Chain Tun, Su Larb Mon, Pyke Tin, I. Kobayashi
第4回韓国‐日本合同シンポジウム (Miyazaki, Japan) 2022.10.31 宮崎大学CADIC
Event date： 2022.10.31
Language：English Presentation type：Symposium, workshop panel (nominated)
Venue：Miyazaki, Japan Country：Japan
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Cow Lameness Detection Using Depth Image Analysis International conference
San Chain Tun, Thi Thi Zin, Pyke Tin, I. Kobayashi
2022 IEEE 11th Global Conference on Consumer Electronics (GCCE) (Osaka, Japan) 2022.10.19 IEEE Consumer Technology Society
Event date： 2022.10.18 - 2022.10.21
Language：English Presentation type：Oral presentation (general)
Venue：Osaka, Japan Country：Japan
In this paper, we introduce to detect cow lameness by using a depth video camera from the top view position. To classify cow lameness, we first extract the sequences of depth value of the cow body region and the maximum value of the back cow area. Then, we will find the average from the maximum height values of the cow backbone area. By using the average values as a feature vector, we classify the cow lameness with the aid of the Support Vector Machine (SVM). To confirm, we perform some experiments by using depth camera images on a real-life dairy farm. The experimental result shows that our proposed method is promising.
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Video-based Automatic Cattle Identification System International conference
Su Larb Mon, Thi Thi Zin, Pyke Tin, I. Kobayashi
2022 IEEE 11th Global Conference on Consumer Electronics (GCCE) (Osaka, Japan) 2022.10.19 IEEE Consumer Technology Society
Event date： 2022.10.18 - 2022.10.21
Language：English Presentation type：Oral presentation (general)
Venue：Osaka, Japan 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(Y ou 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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Cattle Pose Classification System Using DeepLabCut and SVM Model International conference
May Phyu Khin, Thi Thi Zin, Cho Cho Mar, Pyke Tin, Y. Horii
2022 IEEE 11th Global Conference on Consumer Electronics (GCCE) (Osaka, Japan) 2022.10.19 IEEE Consumer Technology Society
Event date： 2022.10.18 - 2022.10.21
Language：English Presentation type：Oral presentation (general)
Venue：Osaka, Japan Country：Japan
This paper proposes the cattle pose classification system by using video-based tracking result. The proposed system is composed of two processes namely feature extraction and classification. In the feature extraction, we employ DeepLabCut network to obtain location feature points which are to be combined with cattle bounding box region values. For classification process, the SVM (Support Vector Machine) classifier will be used. To confirm the proposed method, we tested some experimental results by using the video sequences taken in some real-life dairy farms and classify six poses such as “standing”, “sitting”, “eating”, “drinking”, “sitting with leg extend” and “tail raised”. We got average 88.75% accuracy for all poses.
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Hybrid Approach: Markov Monte Carlo and Naïve Bayes Theorem to Predict Dairy Cow Calving Time International conference
Thi Thi Zin, Swe Zar Maw, Pyke Tin, Y. Horii and H. Hama
16th International Conference on Innovative Computing, Information and Control (ICICIC2022) (Online) 2022.9.15 ICIC International
Event date： 2022.9.15 - 2022.9.16
Language：English Presentation type：Oral presentation (general)
Venue：Online
In this paper, we propose a hybrid approach combing Markov Monte Carlo simulation and the Bayesian method to predict the time to the occurrence of calving events in dairy cows. A total of 30 individual dairy cows were monitored continuously under 24-hour video surveillance prior to calving. The video was annotated for the behaviors of lying, the transition from lying to standing, standing, and the transition from standing to lying for each cow from 72 hours and 168 hours prior to calving. We then derive the corresponding probabilities for each behavior. By using these probabilities, the Markov Monte Carlo simulations are performed to generate the behavior patterns of each cow prior to calving. We then investigate three types of datasets: actual, simulated, and a mixture of the two by using a Naïve Bayes Classifier to perform the prediction process. The experimental results showed that the hybrid approach correctly classified the calving event cent by cent.
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Cattle Lameness Detection by Using Image Processing Technology International conference
T. Onizuka, Thi Thi Zin, I. Kobayashi
16th International Conference on Innovative Computing, Information and Control (ICICIC2022) (Online) 2022.9.15 ICIC International
Event date： 2022.9.15 - 2022.9.16
Language：English Presentation type：Oral presentation (general)
Venue：Online
Lameness is one of the three major diseases of dairy cows, mastitis, reproductive disorders, and limb and hoof diseases. Symptoms of lameness include pain and discomfort in the legs when walking, limping legs, and abnormal movements such as rounding of the back. Among the three major diseases, limb and hoof diseases in dairy cattle are needed to be detected in times, otherwise as the symptoms worsen, diseases such as mastitis and reproductive disorders may occur, leading to a decrease in productivity. Therefore, it is important to detect and treat cows with symptoms of lameness as early as possible. In this paper, we propose a method for cattle lameness detection using an RGB camera and analysing the walking behaviour of cows. Our proposed method can classify cows as healthy or lame with high accuracy. Moreover, we have performed a series of real-life experiments to confirm the effectiveness of the classification results comparing with lameness diagnosis by experts.
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Comparative Study on Color Spaces, Distance Measures and Pretrained Deep Neural Networks for Cow Recognition International conference
Cho Cho Mar, Thi Thi Zin, Pyke Tin, I. Kobayashi, Y. Horii and K. Honkawa5
16th International Conference on Innovative Computing, Information and Control (ICICIC2022) (Online) 2022.9.15 ICIC International
Event date： 2022.9.15 - 2022.9.16
Language：English Presentation type：Oral presentation (general)
Venue：Online
As the cattle monitoring system plays as a crucial role in livestock production and dairy farming to cover the deficiencies in labor for continuous monitoring for each cattle. Robust object detection and tracking system boosts the performance and reliability of autonomous monitoring systems. The recognition task is also important to build the powerful tracking system. The main objective of this paper is to perform the recognition task by analyzing and comparing different color spaces and distance measures to choose the optimal ones. We extracted color moment features and co-occurrence matrix (CM) features from various color spaces: RGB, YCbCr, XYZ, HSV, CIElab, and gray level. We compare those features using different distance measures based on the accuracy values. We also make experiments on different Deep Learning pretrained networks to extract CNN (Convolutional Neural Network) features, and we compared the accuracy values on the classification results with Support Vector Machine (SVM) method. These experiments are tested on the cow dataset that are created from the 2 hours continuous video.
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A Conceptual Framework and Operational Simulation Model for Cattle Lameness Detection International conference
Thi Thi Zin, Pyke Tin
16th International Conference on Innovative Computing, Information and Control (ICICIC2022) (Online) 2022.9.15 ICIC International
Event date： 2022.9.15 - 2022.9.16
Language：English Presentation type：Oral presentation (general)
Venue：Online
In this paper, we introduce a conceptual framework and operational simulation model for cattle lameness detection by using image depth data taken while cattle walking on the pathway from the milking center to the resting area. The proposed conceptual framework maps essential factors that determine lameness scores based on body movement variability. Specifically, body movement variability is to be defined as the root mean square successive differences, various types of information entropies, and geometric measures of the collected sequence of depth data during a field of depth camera view. In addition, we develop an operational simulation model that links the Monte Carlo simulation along with some popular probabilistic distribution functions such as uniform, normal, Poisson, and Gamma distributions for lameness status analysis. The simulation results lead to a conjecture that detection performance and the characteristics of lame and non-lame cows have a large influence on body movement variability. This conjecture will be realized by using real-life data in future work.
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Gama-Markov Branching Process Approach to the Novel Coronavirus (COVID-19) Pandemic International conference
Thi Thi Zin, Pyke Tin and H. Hama
The Fifth International Symposium on Information and Knowledge Management (ISIKM2022) (onsite (Guangzhou, China / Kumamoto, Japan) and online) 2022.3.26 ICIC International
Event date： 2022.3.25 - 2022.3.26
Language：English Presentation type：Oral presentation (general)
Venue：onsite (Guangzhou, China / Kumamoto, Japan) and online
The spreads of the Novel Coronavirus (COVID-19) infectious disease have been increased exponentially in almost all countries around the world. Due to fast in transmission, big in mass and transform in variety, the world health organization has recognized the COVID-19 as a global pandemic. Since then, variety of measures such as mask wearing campaigns, social distance movements, mass vaccination programs and many others were taken globally to reduce the effective reproduction number of an infection below unity. In this paper, we propose a special type of Gama-Markov branching process model to monitor the reproduction mean rate by using data on outbreak size and outbreak duration. We develop a novel method for computing the
probabilities of the rate of spreads, outbreak sizes and outbreak duration. Some simulations based on the daily data appearing on the Internet are performed. The results show that the proposed model has promising potentials for the real-life applications. -
Predicting Calving Time of Dairy Cows by Autoregressive Integrated Moving Average (ARIMA) Model and Double Exponential Smoothing (DES) Model International conference
Tunn Cho Lwin, Thi Thi Zin and Pyke Tin
The Fifth International Symposium on Information and Knowledge Management (ISIKM2022) (onsite (Guangzhou, China / Kumamoto, Japan) and online) 2022.3.26 ICIC International
Event date： 2022.3.25 - 2022.3.26
Language：English Presentation type：Oral presentation (general)
Venue：onsite (Guangzhou, China / Kumamoto, Japan) and online
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Cattle Face Identification with Ear Tags Using YOLOv5s Model International conference
Wai Hnin Eaindrar Mg and Thi Thi Zin
The Fifth International Symposium on Information and Knowledge Management (ISIKM2022) (onsite (Guangzhou, China / Kumamoto, Japan) and online) 2022.3.26 ICIC International
Event date： 2022.3.25 - 2022.3.26
Language：English Presentation type：Oral presentation (general)
Venue：onsite (Guangzhou, China / Kumamoto, Japan) and online
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Black Cow Localization and Tracking with YOLOv5 and Deep SORT International conference
Cho Cho Aye, Thi Thi Zin, I. Kobayashi
The Fifth International Symposium on Information and Knowledge Management (ISIKM2022) (onsite (Guangzhou, China / Kumamoto, Japan) and online) 2022.3.26 ICIC International
Event date： 2022.3.25 - 2022.3.26
Language：English Presentation type：Oral presentation (general)
Venue：onsite (Guangzhou, China / Kumamoto, Japan) and online
Nowadays, precision livestock farming becomes popular fields in agriculture to get a high-quality product. Lacking monitoring livestock overtime can lead them to unexpectedly suffer from serious illness or even life-threatening. Therefore, individual livestock tracking systems can help to understand their actions. The proposed system is emphasized tracking individual black cows after performing cow detection. In this work, YOLOv5 (You Only Look Once) is used for detection and the Deep SORT (Simple Online Real-time Tracking) algorithm is utilized for tracking. In order to enhance the accuracy of the models, transfer learning is adopted. In the detection phase, the YOLOv5 model and appear feature model in Deep SORT were trained with a manually annotated dataset with help of transfer learning. The experimental results reveal that YOLOv5 got detection accuracy of 0.995 mAP@.5 and tracking with Deep SORT achieves the tracking accuracy of 99.4% in testing video-1 and 98.9% in testing video-2.
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工場での作業の見える化 - 作業員のグループ識別及び追跡 - Invited
Thi Thi Zin
令和３年度先端技術研究開発促進・人材育成支援事業：IoT等先端技術利活用セミナー 2022.3.17
Event date： 2022.3.17
Language：Japanese Presentation type：Public lecture, seminar, tutorial, course, or other speech
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Action Recognition System for Senior Citizens Using Depth Image Colorization International conference
Ye Htet, Thi Thi Zin, H. Tamura, K. Kondo, E. Chosa
The 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech 2022) (Osaka, Japan) 2022.3.8 IEEE Life Sciences Technical Community
Event date： 2022.3.7 - 2022.3.9
Language：English Presentation type：Oral presentation (general)
Venue：Osaka, Japan Country：Japan
This paper describes about the system which can be used at the care center for the purpose of elderly action recognition using depth camera. The depth image colorization is used for compression, visualization, and person detection process. The YOLOv5 (You Only Look Once) algorithm is used as object detector. The space-time features are extracted from depth sequences and they are recognized by linear SVM (Support Vector Machine) classifier. The random image sequences are generated for testing to recognize six actions. The results show that this system can detect the various actions with the average of 92% accuracy for different durations.
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A Hybrid Approach: Image Processing Techniques and Deep Learning Method for Cow Detection and Tracking System International conference
Cho Cho Mar,Thi Thi Zin, I. Kobayashi, Y. Horii
The 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech 2022) (Osaka, Japan) 2022.3.8 IEEE Life Sciences Technical Community
Event date： 2022.3.7 - 2022.3.9
Language：English Presentation type：Oral presentation (general)
Venue：Osaka, Japan Country：Japan
Cow detection and tracking system plays an important role in cattle farming and diary community to reduce expenses and workload. This research presents how the conventional image processing techniques can be combined with deep learning concepts to establish cow detection and tracking system. Specifically, we first employ a Hybrid Task Cascade (HTC) instance segmentation network for cow detection. We then built the multiple objects tracking (MOT) algorithm utilizing location and appearance cues (color and CNN features) to carry out cow tracking process. To leverage the robustness of the system, we also considered the recent features from the previous tracked cow.
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A Study on Worker Tracking Using Camera to Improve Work Efficiency in Factories International conference
I. Hidaka, S. Inoue, Thi Thi Zin
The 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech 2022) (Osaka, Japan) 2022.3.8 IEEE Life Sciences Technical Community
Event date： 2022.3.7 - 2022.3.9
Language：English Presentation type：Oral presentation (general)
Venue：Osaka, Japan Country：Japan
The population of Japan is declining every year. As the population declines, the number of employees in enterprises also decreases, and one of the issues for small and medium-sized enterprises is the decline in productivity due to a shortage of employees. It is necessary to improve work efficiency to compensate for the shortage of employees and the resulting decrease in productivity. If changes in the work process and unnecessary movements in the work process can be eliminated, it will lead to shortening of work time and improvement of work efficiency. In this paper, to improve work efficiency for factories, we aim to track the workers in the factory using a camera and show the trajectory of works.