{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/110148"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/110148","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Tracking and Measuring Objects in Obscure Image Scenarios Through the Lens of Shot Put in Track and Field","abstract":"Object tracking and object measurement are two well-established and prominent concepts within the field of computer vision. While the two techniques are fairly robust in images and videos where the object of interest(s) is clear, there is a significant decrease in performance when objects appear obscured due to a number of factors including motion blur, far distance from the camera, and blending with the background. Additionally, most established object detection models focus on detecting as many objects as possible, rather than striving for high accuracy on a few, predetermined objects. One application of computer vision tracking and measurement in imprecise and single-object scenarios is programmatically measuring the distance of a shot put throw in the sport of track and field. Shot put throws in competition are currently measured by human officials, which is both time-consuming and often erroneous. In this work, a computer vision system is developed that automatically tracks the path of a shot put throw through combining a custom-trained YOLO model and path predictor with kinematic formulas and then measures its distance traveled by triangulation using binocular stereo vision. The final distance measurements produce directionally accurate results with an average error of 82% after removing one outlier, an average detection time of 2.9 ms per frame and a total average run time of 4.5 minutes from the time the shot put leaves the thrower's hand. Shortcomings of tracking and measurement in imperfect or singular object settings are addressed and potential improvements are suggested, while also providing the opportunity to increase the accuracy and efficiency of the sporting event.","abstract_html":"Object tracking and object measurement are two well-established and prominent concepts within the field of computer vision. While the two techniques are fairly robust in images and videos where the object of interest(s) is clear, there is a significant decrease in performance when objects appear obscured due to a number of factors including motion blur, far distance from the camera, and blending with the background. Additionally, most established object detection models focus on detecting as many objects as possible, rather than striving for high accuracy on a few, predetermined objects. One application of computer vision tracking and measurement in imprecise and single-object scenarios is programmatically measuring the distance of a shot put throw in the sport of track and field. Shot put throws in competition are currently measured by human officials, which is both time-consuming and often erroneous. In this work, a computer vision system is developed that automatically tracks the path of a shot put throw through combining a custom-trained YOLO model and path predictor with kinematic formulas and then measures its distance traveled by triangulation using binocular stereo vision. The final distance measurements produce directionally accurate results with an average error of 82% after removing one outlier, an average detection time of 2.9 ms per frame and a total average run time of 4.5 minutes from the time the shot put leaves the thrower's hand. Shortcomings of tracking and measurement in imperfect or singular object settings are addressed and potential improvements are suggested, while also providing the opportunity to increase the accuracy and efficiency of the sporting event.","abstract_has_math":false,"creators":["Smith, Ashley Nicole"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Engineering","degree_department":"Electrical and Computer Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Jones, Creed F. III"],"committee_members":["Eldardiry, Hoda","Wyatt, Chris L."],"year":2022,"date_issued":"2022-05-23","date_published":"2022-05-23","updated_at":"2026-07-22T22:19:59Z","subjects":["computer vision","object tracking","object measurement","deep learning","binocular stereo vision","sports"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:34729"],"render_values":[{"text":"vt_gsexam:34729","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/110148","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Jones, Creed F. III"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Eldardiry, Hoda","Wyatt, Chris L."]},{"key":"dc:contributor.department","label":"Department","values":["Electrical and Computer Engineering"]},{"key":"dc:creator","label":"Author","values":["Smith, Ashley Nicole"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-05-24T08:00:23Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-05-24T08:00:23Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-05-23"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computer vision","object tracking","object measurement","deep learning","binocular stereo vision","sports"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:34729"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/110148"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Object tracking and object measurement are two well-established and prominent concepts within the field of computer vision. While the two techniques are fairly robust in images and videos where the object of interest(s) is clear, there is a significant decrease in performance when objects appear obscured due to a number of factors including motion blur, far distance from the camera, and blending with the background. Additionally, most established object detection models focus on detecting as many objects as possible, rather than striving for high accuracy on a few, predetermined objects. One application of computer vision tracking and measurement in imprecise and single-object scenarios is programmatically measuring the distance of a shot put throw in the sport of track and field. Shot put throws in competition are currently measured by human officials, which is both time-consuming and often erroneous. In this work, a computer vision system is developed that automatically tracks the path of a shot put throw through combining a custom-trained YOLO model and path predictor with kinematic formulas and then measures its distance traveled by triangulation using binocular stereo vision. The final distance measurements produce directionally accurate results with an average error of 82% after removing one outlier, an average detection time of 2.9 ms per frame and a total average run time of 4.5 minutes from the time the shot put leaves the thrower's hand. Shortcomings of tracking and measurement in imperfect or singular object settings are addressed and potential improvements are suggested, while also providing the opportunity to increase the accuracy and efficiency of the sporting event."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Object tracking and object measurement are two well-established and prominent concepts within the field of computer vision. While the two techniques are fairly robust in images and videos where the object of interest(s) is clear, there is a significant decrease in performance when objects appear obscured due to a number of factors including motion blur, far distance from the camera, and blending with the background. Additionally, most established object detection models focus on detecting as many objects as possible, rather than striving for high accuracy on a few, predetermined objects. One application of computer vision tracking and measurement in imprecise and single-object scenarios is programmatically measuring the distance of a shot put throw in the sport of track and field. Shot put throws in competition are currently measured by human officials, which is both time-consuming and often erroneous. In this work, a computer vision system is developed that automatically tracks the path of a shot put throw through combining a custom-trained YOLO model and path predictor with kinematic formulas and then measures its distance traveled by triangulation using binocular stereo vision. The final distance measurements produce directionally accurate results with an average error of 82% after removing one outlier, an average detection time of 2.9 ms per frame and a total average run time of 4.5 minutes from the time the shot put leaves the thrower's hand. Shortcomings of tracking and measurement in imperfect or singular object settings are addressed and potential improvements are suggested, while also providing the opportunity to increase the accuracy and efficiency of the sporting event."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Tracking and Measuring Objects in Obscure Image Scenarios Through the Lens of Shot Put in Track and Field"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Jones, Creed F. III"],"dc:contributor.committeemember":["Eldardiry, Hoda","Wyatt, Chris L."],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Smith, Ashley Nicole"],"dc:date.accessioned":["2022-05-24T08:00:23Z"],"dc:date.available":["2022-05-24T08:00:23Z"],"dc:date.issued":["2022-05-23"],"dc:description.abstract":["Object tracking and object measurement are two well-established and prominent concepts within the field of computer vision. While the two techniques are fairly robust in images and videos where the object of interest(s) is clear, there is a significant decrease in performance when objects appear obscured due to a number of factors including motion blur, far distance from the camera, and blending with the background. Additionally, most established object detection models focus on detecting as many objects as possible, rather than striving for high accuracy on a few, predetermined objects. One application of computer vision tracking and measurement in imprecise and single-object scenarios is programmatically measuring the distance of a shot put throw in the sport of track and field. Shot put throws in competition are currently measured by human officials, which is both time-consuming and often erroneous. In this work, a computer vision system is developed that automatically tracks the path of a shot put throw through combining a custom-trained YOLO model and path predictor with kinematic formulas and then measures its distance traveled by triangulation using binocular stereo vision. The final distance measurements produce directionally accurate results with an average error of 82% after removing one outlier, an average detection time of 2.9 ms per frame and a total average run time of 4.5 minutes from the time the shot put leaves the thrower's hand. Shortcomings of tracking and measurement in imperfect or singular object settings are addressed and potential improvements are suggested, while also providing the opportunity to increase the accuracy and efficiency of the sporting event."],"dc:description.abstractgeneral":["Object tracking and object measurement are two well-established and prominent concepts within the field of computer vision. While the two techniques are fairly robust in images and videos where the object of interest(s) is clear, there is a significant decrease in performance when objects appear obscured due to a number of factors including motion blur, far distance from the camera, and blending with the background. Additionally, most established object detection models focus on detecting as many objects as possible, rather than striving for high accuracy on a few, predetermined objects. One application of computer vision tracking and measurement in imprecise and single-object scenarios is programmatically measuring the distance of a shot put throw in the sport of track and field. Shot put throws in competition are currently measured by human officials, which is both time-consuming and often erroneous. In this work, a computer vision system is developed that automatically tracks the path of a shot put throw through combining a custom-trained YOLO model and path predictor with kinematic formulas and then measures its distance traveled by triangulation using binocular stereo vision. The final distance measurements produce directionally accurate results with an average error of 82% after removing one outlier, an average detection time of 2.9 ms per frame and a total average run time of 4.5 minutes from the time the shot put leaves the thrower's hand. Shortcomings of tracking and measurement in imperfect or singular object settings are addressed and potential improvements are suggested, while also providing the opportunity to increase the accuracy and efficiency of the sporting event."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:34729"],"dc:identifier.uri":["http://hdl.handle.net/10919/110148"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["computer vision","object tracking","object measurement","deep learning","binocular stereo vision","sports"],"dc:title":["Tracking and Measuring Objects in Obscure Image Scenarios Through the Lens of Shot Put in Track and Field"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:59Z"}