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The document provides step-by-step setup guidance for running a shuttlecock tracking module in Jupyter/Google Colab: installing libraries, generating frames, labeling images, exporting CSV annotations, creating training/testing NumPy arrays, and training the TrackNet model with specified runtime and parameters. It also outlines environment setup for player service fault detection using MediaPipe pose landmarks, angle calculation, landmark training, model evaluation, and selecting a best-fit Random Classifier for prediction.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":20,"@type":76,"position":81},"https://docshare.wps.com/document/exam/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/configuration-manual-msc-research-project-submission-sheet-project-instructions/291233/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/configuration-manual-msc-research-project-submission-sheet-project-instructions/291233.png","ImageObject",300,407,{"name":92,"@type":93},"Ophelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-21","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What referencing and plagiarism rules are required for this project submission?","Question",{"text":112,"@type":113},"All internet material must be referenced in the bibliography. Using other authors’ written or electronic work without proper citation is illegal (plagiarism) and may lead to disciplinary action.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What are the main steps to set up the shuttlecock tracking module in Jupyter and Google Colab?",{"text":117,"@type":113},"Install and import required libraries, convert video frames to PNG, generate and label images, export labeled outputs to CSV, create heat map predictions, build training/testing NumPy arrays, then train the TrackNet model in Google Colab using a GPU runtime and run accuracy prediction using saved weights.",{"name":119,"@type":110,"acceptedAnswer":120},"How is the player service fault detection environment set up and evaluated?",{"text":121,"@type":113},"Install MediaPipe pose-related libraries, use OpenCV to extract body landmarks from uploaded video frames, compute angles with logical expressions, record landmarks in data frames, train the landmark data, evaluate models to find the best fit, and use the selected Random Classifier for saving and prediction.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},291233,1789991517,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":19,"category_name":20,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":41},7971461741311,"https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826","Configuration Manual  \nMSc Research Project Data Analytics  \nAKSHAY MENON  \nStudent ID: x21173036  \nSchool of Computing National College of Ireland  \nSupervisor: Dr. Paul Stynes Dr. Pramod Pathak  \nNational College of Ireland Project Submission Sheet School of Computing  \n| Student Name: | AKSHAY MENON |\n| --- | --- |\n| Student ID: | x21173036 |\n| Programme: | Data Analytics |\n| Year: | 2022 |\n| Module: | MSc Research Project |\n| Supervisor: | Dr.Paul Stynes Dr.Pramod Pathak |\n| Submission Due Date: | 01/02/2023 |\n| Project Title: | Configuration Manual |\n| Word Count: | XXX |\n| Page Count: | 10 |\n\nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author’s written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \n\n| Signature: |  |\n| --- | --- |\n| Date: | 1st February 2023 |\n\nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST:  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) . | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both foryour own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nConfiguration Manual  \nAKSHAY MENON  \nx21173036  \n1 Initial Environment Setting  \nThe software specification for the setup are described below in the Figure 1 . :-  \nFigure 1: Environment Configuration  \nOnce the envinronment with all valid specification has been setup we will be needing Jupyter notebook and Google Colab to run first module of the reseach project which is shuttlecock tracking .  \n1.1 Initiating Environment  \nLet us first initiate by running module on jupyter notebook.  \n1.Install and import all libraries for running shuttlecock tracking module which requires frame to image conversion as pre-requisite in Fig 2 .  \nFigure 2: Model Libraries  \n2. The Frames from the video are converted into png format through the script in Fig 3 .  \nFigure 3: Frame generation  \n3. The generated frames are stored in local drive which is used for image labelling using Microsoft visual object tagging tool and saving output in csv format which has coordinates of the shuttlecock in the frame in Fig 4 .  \nFigure 4: Image labelling  \n4. The labelled images are used for annotation using Heat Map prediction in Fig 5 .  \nFigure 5: Export to CSV  \nFigure 6: Heat Map Prediction  \n5. The Labelled Csv file and Heat map prediction are used to generate training and testing numpy array file in Fig 6 .  \nFigure 7: Numpy Array files generation  \n6.The training of the shuttlecock will be done on google colab with K8 GPU memory for the same once initiating Google colab notebook change runtime to GPU Kosaian et al.(2021) and mount drive on your google drive in Fig 7 and Fig 8 .  \n7. Upload NPY from local device to google drive for training purpose in Fig 9 .  \n8. From the Google colab run the TrackNet model script in Fig 10 .  \n9.The Model training script needs to be run using labelled csv files and Numpy array files at epoch 30 with tolerance 4 with folder for saving weight in Fig 11 .  \n10. The trained images are predicted using saving weight for the accuracy script has to be run in Fig 12 .  \nFigure 8: GPU  \nFigure 9: Mount Google Driv","cbCairFIyL9Q2hSg","https://ap.wps.com/l/cbCairFIyL9Q2hSg","pdf",2308868,12,"English","# Configuration Manual\n## Initial Environment Setting\n## Initiating Environment for Player Service Fault Detection","[{\"question\":\"What referencing and plagiarism rules are required for this project submission?\",\"answer\":\"All internet material must be referenced in the bibliography. Using other authors’ written or electronic work without proper citation is illegal (plagiarism) and may lead to disciplinary action.\"},{\"question\":\"What are the main steps to set up the shuttlecock tracking module in Jupyter and Google Colab?\",\"answer\":\"Install and import required libraries, convert video frames to PNG, generate and label images, export labeled outputs to CSV, create heat map predictions, build training/testing NumPy arrays, then train the TrackNet model in Google Colab using a GPU runtime and run accuracy prediction using saved weights.\"},{\"question\":\"How is the player service fault detection environment set up and evaluated?\",\"answer\":\"Install MediaPipe pose-related libraries, use OpenCV to extract body landmarks from uploaded video frames, compute angles with logical expressions, record landmarks in data frames, train the landmark data, evaluate models to find the best fit, and use the selected Random Classifier for saving and prediction.\"}]","Configuration Manual - MSc Research Project Submission Sheet - Project Instructions | PDF",1789643819]