[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123747-en":3,"doc-seo-123747-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123747,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Towards Machine Learning Framework for Badminton Game Analysis Using TrackNet and YOLO Models - Master of Science Thesis","Badminton is a widely played sport where reliable game analysis can support athletes, coaches, and researchers, yet many existing solutions remain costly and labor-intensive. This work presents a cost-effective machine learning framework using TrackNet for shuttlecock trajectory tracking and YOLO for player detection and court position estimation from a single smartphone video. With a custom badminton dataset, the study evaluates tracking accuracy and computational efficiency for shuttlecock trajectories, player localization, and shot-type recognition. Results show TrackNet predicts shuttlecock trajectories accurately, and YOLO achieves high-precision detection and shot-type identification, enabling an initial step toward a recommendation system for amateur players.","Towards Machine Learning Framework for Badminton Game Analysis Using TrackNet and YOLO Models  \nby  \nAmna Mohamed  \nA creative component submitted to the graduate faculty in partial fulfillment of the requirements for the degree of  \nMASTER OF SCIENCE  \nMajor: Computer Science  \nProgram of Study Committee:  \nSimanta Mitra, Major Professor Gurpur M Prabhu, Major Professor  \nThe student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this creative component. The Graduate College will ensure this creative component is globally accessible and will not permit  \nalterations after a degree is conferred.  \nIowa State University  \nAmes, Iowa  \n2023  \nCopyright © Amna Mohamed, 2023 . All rights reserved.  \nAbstract  \nBadminton is a popular sport played worldwide, and analyzing the game can provide valuable insights for players, coaches, and researchers. The current methods available for analyzing badminton games can be costly and resource-intensive. Machine learning methods have the potential to automate and enhance the analysis of badminton games. The low-cost implementation of these methods makes them accessible to a wider audience, providing an affordable option for analysis and improvement. In this report, we explore the use of popular object-tracking machine learning methods - TrackNet and YOLO - to analyze badminton games from a single smartphone-recorded video of the game. TrackNet is a deep learning-based algorithm that can track the trajectory of a shuttlecock, while YOLO is an object detection algorithm that can identify players and their positions on the court. Using a custom dataset of badminton games, we evaluate the performance of these methods in terms of accuracy and computational efficiency in tracking the shuttlecock, tracking players, and identifying shot types. Our results show that TrackNet can accurately predict the trajectory of the shuttlecock, while the YOLO model can identify players and detect shot types with high precision. This research introduces a cost-effective analysis framework that represents the initial stage in developing a recommendation system aimed at amateur players to enhance their gameplay.  \nAcknowledgements  \nContents  \nAbstract i  \nAcknowledgements ii  \nList of Figures v  \nList of Tables vi  \n1 Introduction 1  \n2 Literature Review 3  \n2.1 Overview .................................... 3  \n2.2 Background ................................... 3  \n2.2.1 Ball Tracking .............................. 3  \n2.2.2 Object Detection & Tracking ..................... 4  \n2.2.2.1 Traditional methods ..................... 4  \n2.2.2.2 Deep learning-based methods ................ 5  \n2.2.3 Action Recognition ........................... 6  \n2.3 Related Work .................................. 7  \n3 Methodology and Design 9  \n3.1 Overview .................................... 9  \n3.2 Shuttlecock Tracking Using TrackNet ..................... 10  \n3.2.1 Shuttlecock Data Collection ...................... 11  \n3.2.2 TrackNet Training ........................... 12  \n3.3 Player Tracking and Shot Type Detection using YOLOv5 ......... 13  \n3.3.1 Player/Shot-Type Data Collection .................. 15  \n3.3.2 YOLOv5 Training ........................... 16  \n4 Results and Discussion 17  \n4.1 Overview .................................... 17  \n4.2 Experimental Setup .............................. 17  \n4.3 Evaluation Metrics ............................... 18  \n4.4 Shuttlecock Tracking Experimental Results ................. 19  \nContents iv  \n4.4.1 Dataset ................................. 19  \n4.4.2 TrackNet Results ............................ 19  \n4.4.3 Discussion ................................ 20  \n4.5 Player Tracking and Shot Type Detection Experimental Results ...... 21  \n4.5.1 Dataset ................................. 21  \n4.5.2 YOLOv5 Results ............................ 21  \n4.5.3 Discussion ................................ 2","cbCaimOt5ulhTRn0","https://ap.wps.com/l/cbCaimOt5ulhTRn0","pdf",6388803,1,39,"English","en",105,"# 1 Introduction\n# 2 Literature Review\n## 2.1 Overview\n## 2.2 Background\n## 2.3 Related Work\n# 3 Methodology and Design\n## 3.1 Overview\n## 3.2 Shuttlecock Tracking Using TrackNet\n## 3.3 Player Tracking and Shot Type Detection using YOLOv5\n# 4 Results and Discussion\n## 4.1 Overview\n## 4.2 Experimental Setup\n## 4.3 Evaluation Metrics\n## 4.4 Shuttlecock Tracking Experimental Results\n## 4.5 Player Tracking and Shot Type Detection Experimental Results\n# 5 Conclusion\n## 5.1 Summary of Work\n## 5.2 Limitations\n## 5.3 Future Work\n# Bibliography\n# Appendix A","[{\"question\":\"What machine learning methods are used to analyze badminton games in this thesis?\",\"answer\":\"The thesis uses TrackNet for shuttlecock trajectory tracking and YOLO (YOLOv5) for player detection and shot-type recognition from video.\"},{\"question\":\"How is the analysis performed from the input data?\",\"answer\":\"The system analyzes a single smartphone-recorded video, tracking the shuttlecock’s path and identifying players and their positions on the court, then inferring shot types.\"},{\"question\":\"How are the models evaluated and what do the results show?\",\"answer\":\"Performance is assessed in terms of accuracy and computational efficiency for shuttlecock tracking, player tracking, and shot-type identification. The results indicate TrackNet accurately predicts shuttlecock trajectories and YOLO identifies players and shot types with high precision.\"}]","Towards Machine Learning Framework for Badminton Game Analysis Using TrackNet and YOLO Models - Master of Science Thesis | PDF",1785818312,98,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"towards-machine-learning-framework-for-badminton-game-analysis-using-tracknet-and-yolo-models-master-of-science-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/towards-machine-learning-framework-for-badminton-game-analysis-using-tracknet-and-yolo-models-master-of-science-thesis/123747/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What machine learning methods are used to analyze badminton games in this thesis?","Question",{"text":75,"@type":76},"The thesis uses TrackNet for shuttlecock trajectory tracking and YOLO (YOLOv5) for player detection and shot-type recognition from video.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the analysis performed from the input data?",{"text":80,"@type":76},"The system analyzes a single smartphone-recorded video, tracking the shuttlecock’s path and identifying players and their positions on the court, then inferring shot types.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the models evaluated and what do the results show?",{"text":84,"@type":76},"Performance is assessed in terms of accuracy and computational efficiency for shuttlecock tracking, player tracking, and shot-type identification. The results indicate TrackNet accurately predicts shuttlecock trajectories and YOLO identifies players and shot types with high precision.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]