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RochesterInstituteofTechnology
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Theses
12-2021
Camera-baseddeeplearningAIassistantsystemforbasketballtraining
GuangkunZeng
gz4641@
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CAMERA-BASEDDEEPLEARNINGAIASSISTANTSYSTEMFORBASKETBALLTRAINING
RIT
Camera-baseddeeplearningAIassistantsystemforbasketballtraining
By
GuangkunZeng
AThesisSubmittedinPartialFulfillmentoftheRequirementsfortheDegreeofMasterofFineArtinVisualCommunicationDesign
School/DepartmentofDesignCollegeofArtandDesignRochesterInstituteofTechnology
Rochester,NYDecember,2021
ThesisApproval
Camera-baseddeeplearningAIassistantsystemforbasketballtraining
ThesisTitle
GuangkunZeng
ThesisAuthor
Submittedinpartialfulfillmentoftherequirementsforthe
degreeofMasterofFineArts
TheSchoolofDesign|VisualCommunicationDesign
RochesterInstituteofTechnology|Rochester,NewYork
MikeStrobert ChiefThesisAdvisor
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CAMERA-BASEDDEEPLEARNINGAIASSISTANTSYSTEMFORBASKETBALLTRAINING
Abstract
TheYOLO,aComputerVisionAlgorithms,isbroughtouttoanalyzethebasketballplayer’sstatusasadataset.Itcanrecordtheplayers’behavioronthecourtincludingdribbling,shooting,andrunning.Inthisway,theappcouldcollectthefieldgoalyoumadeandmissed.First,youshouldusethisapptorecordavideoofyourshoottraining.Afterthat,theAIwouldanalyzeandbringsouta3dvirtualdiagraminterpretyourperformance.Thisdiagramwillshowthehotzoneandcoldzoneforyourfieldgoal.Also,thetrackofyourballwillbedisplayedonthevideosothatyoucanknowiftheangleofyourshootingistoolowortoohigh.Intheend,theAI-basedonmachinelearningwillgiveoutaplanaccordingtoyourperformanceonshooting.
Asatrainingmobileapplicationsupportedbycamera-basedactionrecognition,thetargetaudienceisthebasketballamateurplayerswhodon’thavetheresourcesasproplayersdo.Thisprojectwillbedesignedasanewtrainingexperienceandwillbedeliveredasapromovideothatshowshowtousetheapplicationandalsothescenariopeopleuse.
Keywords
MR,virtualenvironment,camera-based,trainingsystem,deep-learning,machinelearning,motioncapture,AI
CriticalAnalysisandSummary
Context
Manybasketballenthusiastswanttoupgradetheirskillstoafarbetterlevel,butunfortunately,noteveryonehasthefundstohireprofessionalcoachestotrainonthebestcourt.Andwhat’smore,manyamateurplayersdonotevenknowhowtopracticeinaproperwaybecausethevideotutorialisnotasintuitiveastherealcoachorastheprofessionalhumancoachis.What’smore,itisalmostimpossibleforthebasketballplayertoquantifyhowgoodhis/hershootingis.Butpeopledoneedverygoodshootingformandtechniquetoshootconsistently.Howmightweimproveplayers’trainingexperience?
Nowadays,MR(mixedreality)haschangedourlife.ResearchbyVisualCapitalistprojectsthattheXRmarketwillbeworth$209billionby2022,markinganeight-foldincreasefrom20181.Furthermore,63%ofshareholdersinXRtechnologycompaniesbelievethetechnologywillbemainstreamby2024.Atthesametime,MixedRealityisquitewithinthemiddlebutalsothelongertermoftheentertainmentindustry.2Thistypeoftechnologycouldprovideacustomizedanduniquetrainingexperienceevencomparedtothebestcourtandtrainerscoulddo.3
TheMixedRealityisthetechnologythatcouldprovideanimmersiveexperienceanddirectdatavisualization.IproposetobringoutaMixedRealitymobileapplicationthatcangenerate3dvirtualdiagramofthebasketballcourttoassistplayerstounderstandtheirshootingperformance.Atthesametime,theappwillcollecttheplayer’sdataandmakethetrainingplanaccordingtotheAI-basedanalysissystem.
Methodology
Toimplementthissystem,multipletechnologieswillbeintegrated.TheYOLO,aComputerVisionAlgorithmswillrecognizetheballtraceandcollectdataofyourtraining.4Themotioncapturewith3Dvirtualenvironmenttechnologywillinterprettheshootinggestureandmovement.Thenthemachinelearningwillgenerateaspecifictrainingplan.
Thephaseofinterviewanduserresearchwillnotproceedbecausethisdesignputsmoreemphasisonconceptualdesign.Thisdesignfocuseson3problemsthatplayersoftenmeet.First,it’shardforamateurplayerstoquantifytheirskilllevel.Second,noteveryplayercanmakeapreciseplanasprofessional
1Emrich,T.(2020,February25).20for2020:AugmentedRealityTrendsandHowTheyMayPlayOutThisYear[Weblogpost].Retrievedfrom
/@tomemrich/20-augmented-reality-trends-to-keep-
an-eye-on-in-2020-d2b0258edbb
2Terry,Q.(2019,July23).ARiselevatingtheplayingfieldforsportsbycreatingsmartertrainingmethods.Retrievedfrom/futuresin/ar-is-elevating-the-playing-field-for-sports-by-creating-smarter-training-methods-77db01a84d64
3Lee,David."OurFirstShot(s)."Medium(blog).July17,2018./nex-team/our-first-shot-s-272c67d0349d.
4Terry,Q.(2019,July23).ARiselevatingtheplayingfieldforsportsbycreatingsmartertrainingmethods.Retrievedfrom/futuresin/ar-is-elevating-the-playing-field-for-sports-by-creating-smarter-training-methods-77db01a84d64
coachescando.Third,playersarenotsurethepracticeisexecutedperfectly.Peoplealsosometimesforgothowmanyshotsthey’vemade.
Threesolutionshavebeengivenout:First,A3ddiagramofyourplayscanhelpuserstoquantifytheirperformance.ThenAIDeeplearningcangiveyouadviceasgoodasacoachdoesorevenbetter.Finally,Asmartphonecamera-basedreal-timesystemcanrecordyourplayswithnomistakes.
Astheresultoftheproblemsolutions,HoopLabidentified3specificdesigngoalsthatareusedtoprototype:
Friendlytouse
Createanexperiencethatuserscanenjoy.Userscancustomizetheir3davatarandpickupfavoriteclothandhaircutsforit.Theavatarwillberiggedandmotioncapturedbyusersasawaytounderstandyourshootingmovement.
Easytounderstand
A360degrees3Ddiagramwillbegeneratedaccordingtotheplayrecording.Itisabletoturnyourstillcamerarecordinginto3dversionenvironment.TheAIwillcalculatethedistancebetweenobjects(player-player,shooter-hoop,defender-ball)
Convenienttorecord
Theuseronlyneedsasmartphonecameratodothebasicfunction.Usingasmartwatchtounlockadvancedfeatures.
Afterthreeroundsofprototyping,thecorrespondinginterfacelo-fiwireframesaredesigned.Basedonthoseinteractionwireframes,theoutputoftheUIvisualwireframeisfinallycompleted.ApromovideothatcombinedUIelementsandapplicationfunctionsisalsobemade.
Agamedesigntheoryisalsobroughtinforthissystem.Userscanearncoinsbyfinishingthetasks.Thentheycanusegamecoinstounlockavatarsandnewadvancedtasks.Thecirculareconomyisformedinthisprocess.
Ithinktherewillbesustainableiterationstoevaluatemyproposal.Atphaseone,whichistheinitialstateofaproduct,alargenumberofA/Btestswillbegiventotheusers.AccordingtotheA/Btestresultsthatwecollect,itcanbedecidedwhichfeatureisbetterforusers.Inphasetwo,theproductstartstooperatesmoothly,thenplentyofdatacanbeanalyzedbymachinelearning.DuetoAI,wecandecidewhatnewfeaturesthatmaysatisfyusersshouldbebuiltfurther.
Conclusion
It’sdifficulttoquantifytheexperienceofthesystem.Ifthesystemcouldpresentacustomizedtrainingplanfortheuserandcreateavirtual3denvironmentofarealcourtthattheplayerdoesn’tfeelstrangeaboutit,thisdesignwillbeasatisfiedsolution.
However,withthelimitofreal-timerendertechnologyandhardwarehashingpower,itisstillimpossibletoimplementanapplicationlikethisinamobilephone.ThisprojectisanapproachthatrepresentsonepossibilityofwhatAIalgorithmscandoforsportsgames.
AppendixA:ExpandedThesisDefensePresentation
Initialdesign
Designintension
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