CoachAI: A Project for Microscopic BadmintonMatch Data Collection and Tactical Analysis
Tzu-Han Hsu† Ching-Hsuan Chen† Nyan Ping Ju† Tsı-Uı Ik†∗ Wen-Chih Peng†
Chih-Chuan Wang‡ Yu-Shuen Wang† Yuan-Hsiang Lin Yu-Chee Tseng†
Jiun-Long Huang† Yu-Tai Ching††Department of Computer Science, College of Computer Science
National Chiao Tung University1001 University Road, Hsinchu City 30010, Taiwan
∗Email: [email protected]
Abstract—Computer vision based object tracking hasbeen used to annotate and augment sports video. For sportslearning and training, video replay is often used in post-match review and training review for tactical analysis andmovement analysis. For automatically and systematicallycompetition data collection and tactical analysis, a projectcalled CoachAI has been supported by the Ministry ofScience and Technology, Taiwan. The proposed projectalso includes research of data visualization, connectedtraining auxiliary devices, and data warehouse. Deeplearning techniques will be used to develop video-basedreal-time microscopic competition data collection based onbroadcast competition video. Machine learning techniqueswill be used to develop tactical analysis. To reveal datain more understandable forms and to help in pre-matchtraining, AR/VR techniques will be used to visualize data,tactics, and so on. In addition, training auxiliary devicesincluding smart badminton rackets and connected servingmachines will be developed based on the IoT technologyto further utilize competition data and tactical data andboost training efficiency. Especially, the connected servingmachines will be developed to perform specified tacticsand to interact with players in their training.
Index Terms—Badminton, broadcast video, competi-tion data collection, tactical analysis, computer vision,deep learning, machine learning, TrackNet, YOLOv3,OpenPose, visualization, cloud service, smart racket, pro-grammable serving machine
‡Office of Physical Education, National Chiao Tung University,Hsinchu City 30010, Taiwan.Department of Electronic and Computer Engineering, National
Taiwan University of Science and Technology, Taipei City 106,Taiwan.
I. INTRODUCTION
In the high-rank badminton competitions, theoutstanding performance of athletes depends on notonly the persistent and solid training but also themastery of the opponents’ superiority, inferiority,and even tactics and emotion in the games. Hence,supporting systems for real-time tactical informationcollection and analysis are critical and significantfor top athletes to win their games. Possible servicesduring matches may include monitoring of ball typeusage and ball accuracy, evaluating players’ reactionagility and footwork techniques, alerting happeningof continuous loss and analyzing the causes, anddetecting modes of defensive or offensive playing.According to the information, coaches can adviseplayers to take corresponding tactics. Furthermore,in the post game review, such a system can providesaccurate gaming data for the discussion. Therefore,automatic competition data collection and tacticalanalysis are turnkey technologies for professionalathletes.
Videos can be considered as logs of visual sensorsand retain a large amount of information. In mostcases, tactical data were manually labeled fromvideo, but this approach is time-consuming andlaborious. In the last few years, smartphone appshave been used to assist on-site labeling. Due tothe fast rhythm of the games, mistakes happenfrom time to time. It would be efficient to extractcompetition data from video based on computervision techniques, and real-time tactical analysis
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incorporated with big data analysis could be real-ized. This method not only reduces manual errors,but also improve efficiency and accuracy. Moreover,beyond macroscopic data such as ball types, com-puter vision can provide microscopic data such asshuttlecock trajectories and player postures frameby frame. Furthermore, AR/VR can help coachesand athletes to easily catch the key information andutilize the information in their training.
The Hawk-eye [1] is a multiple cameras sys-tem that can calculate 3D ball trajectories and hasbeen widely used in various international com-petitions such as football, cricket, tennis, etc. toassist umpires in the judgments of controversialballs. In NBA, professional companies deploy high-resolution cameras in arenas incorporated with of-fline image processing for the tracking of the balland players. However, these systems are expen-sive and accompanied high operating cost. In thestudy of shuttlecock trajectory prediction [2], 2Dlaser scanners are used to locate the badmintonshuttlecock in the real world environment. In addi-tion, shuttlecock speed and orientation, etc. can beknown. However, the equipment is not popular andlead to limited application. In [3], computer visionincorporated with machine learning are applied todetect the court and players and classify ball types.The data of player positions and ball types areused in the classification of tactics. Most worksmentioned require the assistance of experiencedcoaches. That hinders the development of automaticdata collection systems.
In this paper, a project for badminton competi-tion data collection and tactical analysis is intro-duced. Fig. 1 depicts the scope of this project, amobile computing and cloud service based solu-tion. The turnkey techniques include deep learn-ing video analysis, machine learning and big datatactical analysis, wearable sensor and IoT technol-ogy, and AR/VR visualization. The data collectionsub-project utilizes broadcast videos from publicmedias such as the BadmintonWorld.TV channelon YouTube or videos taken by mobile devices orconsumer cameras to efficiently collect microscopiccompetition data by deep learning video analy-sis. TrackNet [4] is used to track the shuttlecock,YOLOv3 [5] is used to locate the players, andOpenPose is used to detect the skeletons of the
players. The tactics sub-project develops ball typeclassification and spatial and temporal tactical anal-ysis. Real-time court-side feedback will be possiblefor coaching via mobile devices. The visualiza-tion sub-project provides graphic user interfacesand data representation for easy understanding andspeedy knowledge discovery. The data warehousesub-project provides the data exchange platform andkeeps all microscopic and macroscopic competitiondata for further analysis. Most of the services will behosted on cloud platforms. In addition, an IoT-basedserving machine system that can be programmedto perform specified tactics will be developed tointeract with players in their training.
Fig. 1. The scope of CoachAI.
Overall speaking, we are going to build the ca-pability of microscopic data collection and tacticalanalysis of badminton competitions. In addition,visualization techniques such AR/VR will be usedto help data presentation and data mining, and con-nected serving machines and smart rackets will bedeveloped to boost training efficiency. Finally, back-end servers will be built to host cloud services anddata warehouse. The rest of this paper is organizedas below. Section II presents the architecture of theproposed system in the CoachAI project. In SectionIII, the deep learning network for shuttlecock tra-jectory tracking is described. Section IV introducesthe deep learning networks for player positioningand skeleton detection. Section V provides currentimplementation status. The conclusion and futureworks are given in Section VI.
II. OVERVIEW OF THE COACHAI PROJECT
The scope of the CoachAI project is illustrated inFig. 1. The data collection sub-project, Sub-project
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1, is going to build the capability of automaticmicroscopic data collection based on deep learningtechnology. The videos of top-rank professionalbadminton competitions, that are available on pub-lic domain such as All England Open BadmintonChampionships on YouTube, are the subjects to beanalyzed. The frame-level microscope data includethe position of the shuttlecock, the locations andskeletons of the players. For each frame, a deeplearning network, called TrackNet, that can locatehigh-speed and tiny objects from ordinary broadcastvideo is adopted to position the flying shuttle-cock; and the well known YOLOv3 and OpenPosepackages are fine-tuned to segment players anddetect the skeletons of players, respectively. Moremacroscopic data, such as shuttlecock speeds, stroketimes, ball types and footworks, can be availablebased on the microscope data.
The tactical analysis sub-project, Sub-project 2,is going to build the capability of tactical analysisand recommendation based on machine learningtechniques and big data analysis. The tactical infor-mation not only will be provided for athletes andcoaches’ reference but also can be further used toprogram the connected serving machines to boostthe training efficiency. Classifiers will be designedto identify stroke times, recognize ball types andproactive/active modes of playing, and predict thedrop point of the shuttlecock. Statistic analysis canbe applied to analyze the footwork patterns and tofind the reasons of continuous loss. Spatial analysiscan help to understand the ability of ball control andto find ball usage patterns. Temporal analysis can beused to estimate physical declination and to detecttactical change. During the matches, the capabilityof real-time tactical analysis is important in order toprovide tactical information for coaches and players.
The visualization sub-project, Subproject 3, pro-vides graphic user interfaces, image processingfunctionality, and AR/VR capability. By the helpof Subproject 3, the data obtained by Subproject1 and Subproject 2 can be presented in the waysof easy understanding. That not only helps athletesand coaches to quickly catch the insight of the databut also benefits data mining processes. Further-more, virtual reality (VR) and augmented reality(AR) techniques can help athletes familiar withopponents’ ball type usage, footwork, and tactics in
immersed ways. That can strengthen psychologicalquality of players.
In addition to data collect and tactical analysis,training auxiliary devices can directly benefit train-ing efficiency. The training assistant sub-project,Subproject 4, is going to develop a real-time trainingauxiliary system including smart badminton rack-ets and connected serving machines. Besides thecomputer vision based ball type classification, smartrackets are designed to recognize stroke types byutilizing wearable IMU sensors installed at the bot-tom of the racket handle. The wearable sensors arecomposed of MPU9250 and Nordic nRF52 chips.The MPU9250 chip has a 3-axis accelerometer anda 3-axis gyroscope, and the nRF52 chip supports theBluetooth 4.0 protocol stack. An app is developedto collect the IMU data and classify strokes. Thesystem architecture of the smart rackets is illustratedin Fig. 2, and the app UI is depicted in Fig. 9.In addition, IoT-based connected serving machinesprovide APIs for programmable online operation.Therefore, the serving machines can simulate op-ponent’s ball usage patterns to reinforce pre-matchtraining.
Fig. 2. The architecture of Smart rackets.
The data warehouse sub-project, Subproject 5,provides database and cloud computing services.It stores a large amount of video data, trajectorydata and sensor data to support the development ofother sub-projects, especially for tactical analysis.Besides, it provides cloud computing services suchas deep learning, big data analysis or other complexoperations. Parallel computing will be used to ac-celerate calculation, and docker technology will beintroduced to fully utilize GPU resources.
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We cooperate with the school badminton teamof NCTU as well as top-ranked athletes in Taiwanthrough the coordination of the Chinese TaipeiBadminton Association. To help national athletesto improve their performance, the developed tech-niques will be used to collect top 16 athletes inthe world ranking in the future. At the currentstage, the NCTU badminton team members providetheir professional knowledge on data labeling anddata representation. They also provide professionalconsultation on tactical analysis and data presenta-tion. The prototype system will be assessed by theschool team to take feedback from the perspectiveof professionals. The value of the prototype systemwill be finally evaluated by the national athletes. Weexpect to provide players with a big data analysissystem that combines real-time strategy analysisand training-assisted devices. By Strategy Recom-mendation system, players can adjust their tacticduring the game instantly. By real-time trainingauxiliary devices, players can adjust the trainingcontent to improve the training efficiency. The pro-posed system can enable players to have outstandingperformance and provide players a better trainingenvironment.
III. SHUTTLECOCK TRAJECTORY TRACKING
It is challenging to track small objects from lowresolution or low quality video. Even worse is thatfor sports like tennis, badminton, and baseball, dueto the small size and high flying speed of the balls,the images of the balls in the video are typicallyblurred, and the ball recognition and positioning be-come harder. In addition, occlusion is another issuethat may hinder recognition algorithms. Currently,commercial solutions in the market usually rely onhigh resolution and high frame rate video. However,the hardware investment and operation cost are alsohigh.
A convolutional neural network (CNN) basedframework, called TrackNet [4], has been proposedto take consecutive frames together to generatea heatmap for ball detection. Fig. 3 depicts theframework of TrackNet. In the figure, TrackNetis designed to process three consecutive frames atonce, and to output a ball detection heatmap corre-sponding to the last frame. Since three frames areinput together, not only the appearance but also the
trajectory of the shuttlecock could be cues for balldetection and positioning. In this work, we adpotedTrackNet to track the high speed shuttlecock frombroadcast videos or those token by consumer mobiledevices such as smartphones.
Fig. 3. The architecture of TrackNet
For training and testing purposes, a dataset basedon videos available on Youtube with resolution1280 × 720 and then downsampled to 640 × 480has been prepared. TrackNet is trained to generate aprobability-like detection heatmap that has the samesize as the input images. The first 13 layers referringto the design of VGG16 [6] whose input data arrayhas dimension 640×480 is for the purpose of featureextraction, and the 14th to 24th layers referring toDeconvNet [7] follows to perform upsampling. Atthe end, a ball detection heatmap is generated byapplying pixel-wise softmax. The ground truth ofa heatmap is an scaled 2D Gaussian distributionlocated at the head of the shuttlecock. The ballcenter is available in the dataset, and the varianceof the Gaussian distribution refers to the size ofshuttlecock images. The formula is showed in Eq.(1).
G(x, y) = b( 1
2πσ2e−
(x−x0)2+(y−y0)
2
2σ2 )(2πσ2 · 255)c(1)
The first part is a Gaussian distribution centered at(x0, y0) with a variance σ2. The second part scalesthe value to the range [0, 255]. Since the averageradius of the shuttlecock in the images is about 5pixels, we set σ2 = 10. Fig.3 depicts an example ofthe heatmap function.
The detection heatmap is not directly used in thecalculation of the loss function, and therefore is notconsidered as part of the deep convolution network.
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Fig. 4. Heatmap example.
The last second layer that is the one right before thesoftmax operation has size 640×480 and depth 256.The size is the same as the input image size, and thedepth is corresponding to the grayscale values from0 to 255. The pixel-wise one hot encode is used toencode heatmap into the output matrix. The pixelwith the largest value on the heatmap is consideredas the location of the shuttlecock. In the trainingphase, the cross-entropy function is used to calculatethe loss function of P (i, j, k). P (i, j, k) denotesthe pixel-wise probability of gray-scale value k atposition (i, j), and Q(i, j, k) is the correspondingground truth. Let HQ(P ) denote the loss function.Then,
HQ(P ) = −∑i,j,k
Q(i, j, k)logP (i, j, k) (2)
After all, if a detection heatmap is given, the po-sition of the shuttlecock can be decided as follows.First, the heatmap is converted into a black-whitebinary map by a threshold. If the value of a pixel onthe heatmap is greater than the threshold, the outputof the pixel is set to 255. Otherwise, the output is setto 0. Then, a circle finding algorithm named HoughGradient Method is applied to find the center of eachspot. If exactly one circle is identified, the center ofthe circle is reported as the location of a shuttlecock.In other cases, the image is considered as withouta shuttlecock.
IV. ATHLETE POSTURE DETECTION
In order to evaluate the movements of players,the posture data are important. The collection ofposture data is divided into two stages, the boundingbox detection and the skeleton detection. YOLOv3
[5] is used in the player bounding box detection,and OpenPose [8] is used in the player skeletondetection.
Object detection needed in many computer visionapplications is one of the most early studied prob-lems in deep learning. The R-CNN family [9]–[11]and YOLO family [12] are two main frameworksin object detection. R-CNN-like networks first gen-erate many region proposals each of which mightenclose an object, and then detect and classify theobject in each region proposal. To speed up theprocessing speed, YOLO-like networks apply end-to-end philosophy. A set of pre-defined grid-basedregions will be examined to estimate the probabilityof existing some kinds of objects. YOLO’s are con-sidered as good choices for real-time applications.YOLOv3 is currently one of the widely used objectdetection networks. It is worth mentioned that atresolution 320 × 240, YOLOv3 runs in 22 ms perimage and reaches accuracy 28.2 mAP, as accurateas SSD but three times faster [5]. Hence, in thiswork, YOLOv3 is adopted to get player boundingboxes.
The YOLOv3 pre-trained model is used to detectthe bounding boxes of players in video. However,YOLOv3 marks all of the people in the picturesincluding non-players. Therefore, based on the in-formation of the badminton court layout, an homog-raphy matrix is calculated to estimate the projection2D coordinate on the court. The midpoint on thelower boundary of a bounding box is considered asthe 2D coordinate of the object that is used to filterout non-plyers’ bounding boxes. The bounding boxcoordinates which are outside the court region willbe filtered out. Based on the information collected,we can roughly locate and analyze the player’sposition on the field.
Besides the bounding boxes that can give theposition information, the skeletons of players cantell the movement details of players. The bound-ing boxes of players are enlarged by 0.5 timesto enclose a whole image of the player. Then,OpenPose [8], which was developed by CarnegieMellon University (CMU), is adopted to depict theskeleton on the MPII model for the human near thecenter of the bounding box. Players on both sidesare processed separately. In the MPII model, thehuman body is sketched in 15 keypoints. Based
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on the series of skeletons, the player’s footworkand physical movements can be understood andanalyzed. Furthermore, the playing modes of playersin the matches, including defensive playing andoffensive playing, could be classified. In the future,a new skeleton model that is composed of theMPII skeleton model as well as an extra keypointrepresenting the racket will be trained.
In summary, combined with the bounding boxesof players obtained by YOLOv3 and the skeletonsof athletes obtained by OpenPose, the movementsand footwork can be analysis. Moreover, big dataanalysis will be introduced to improve the efficiencyof players’ training and event to adjust playingtactics in their matches.
V. IMPLEMENTATION
In this section, we introduce some preliminary re-sults of the CoachAI project. First of all, for trainingand testing, two matches of Tai Tzu-Ying in 2018All England Open have been manually labeled, in-cluding Tai Tzu-Ying vs. Akane Yamaguchi and TaiTzu-Ying vs. Chen Yufei. There are about 150,000frames in the two match videos. In the frame level,the shuttlecock position, bounding boxes of players,and skeletons of players were labeled. In the rally-level, the shuttlecock hit time and ball types wereannotated, and the reasons of score or loss were alsoremark. The rally-level labeling is implemented bythe members of the NCTU school badminton team.
A badminton match can usually last for near anhour. The video may have nearly 100,000 frames.The labeling task is quite laborious. Therefore, avideo preprocessing tool helped to mark out non-competition screens, and also provided rough startand finish times of rallies. To increase the efficiencyof skeleton labeling, YOLOv3 was used to segmentplayers. After magnifying the edges of the playerbounding box by 1.5 times, the segmented imagewas analyzed by OpenPose package to detect theskeleton in the MPII model. The skeleton dataare clustered to identified outliers. Only skeletonsconsidered as outliers are manually labeled. Fig. 5illustrates the process. The clusters of skeletonsdetected by the MPII pre-trained model are sketchedin the box on the left hand side. If a outlier clusteris selected, many incorrect skeletons with imagesare displayed. See the pictures in the box at the
middle. Only those skeletons will be adjusted. Seethe pictures in the box on the right hand side. Afterthe skeletons are corrected, the new dataset is usedto fine-tune the model. In the future, we will adda keypoint corresponding to the racket neck join inour skeleton model.
Fig. 5. Flow chart for skeleton adjustment.
For the labeling of shuttlecock, due to the highmoving speed, the shuttlecock images in the videomay be blur and with afterimage trace. In suchcases, the latest ball image is labeled to be theposition of the shuttlecock. This method is the sameas the method of labeling tennis proposed in thework of TrackNet [4]. The racket keypoint is labeledat the join of the neck and face of the racket. Forexample, in Fig. 6, the shuttlecock is flying in thedirection from the top-side player to the bottom-sideplayer. Therefore, the shuttlecock is labeled at thered dot. In addition, the racket of the top-side playeris labeled at the yellow dot.
Fig. 6. An example of frame-level labeling: This picture is capturedfrom the video of 2018 Indonesia Open Final Match [13].
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Tactical analysis and visualization can provide usdetails and insights of a badminton match. Fig. 7shows an example of the distribution of stroke typesand the statistics of loss reasons in a game. It canbe observed that the two players use a slightlydifferent stroke type. More rally-level informationis illustrated in Fig. 8. The left sub-figure shows thestroke count for each rally. The color at the verticesdenotes the player who scored this rally. As a vertexis clicked, a radar chart like the right sub-figure willpop up to show the statistics of ball type usage inthis rally.
Fig. 7. Left: the distribution of the ball type usage. Right: thedistribution of the reasons of loss.
Fig. 8. Left: the statistics of stroke count per rally; Right: thedistribution of ball type usage in a rally.
Fig. 2 illustrates the system architecture of thesmart badminton racket. Fig. 9 shows the UI ofthe smart racket system. When receiving the signalof strokes from the smart racket via Bluetooth,the system classifies the strokes and shows theresults on the interface instantly, as illustrated inFig. 9. Currently, seven common stroke types can berecognized, including cut, drive, lob, long, netplay,rush and smash. The UI has been developed as amobile app and on web platform.
Fig. 9. The UI of the smart badminton racket system.
VI. CONCLUSION
In this paper, a project for badminton competi-tion data collection and tactical analysis has beenintroduced. Deep learning techniques are adoptedto develop video-based competition data collection,including shuttlecock trajectory and players’ posi-tions and skeletons. Based on the microscopic data,machine learning techniques are used to developtactical analysis. Visualization techniques are usedto properly and meaningfully represent and displaymicroscopic and macroscopic competition data foreasy understanding. Besides, training auxiliary de-vices including smart badminton rackets and con-nected serving machines will be developed in orderto improve the training efficiency. In the future,besides developing the techniques depicted in theroad-map, we would like to develop the capabilityof 3D data collection and analysis, and extend theresearch on singles matches to doubles matches.
ACKNOWLEDGEMENT
This work of T.-U. Ik was supported in part by theMinistry of Science and Technology, Taiwan undergrant MOST 107-2627-H-009-001 and MOST 105-2221-E-009-102-MY3. This work was financiallysupported by the Center for Open Intelligent Con-nectivity from The Featured Areas Research CenterProgram within the framework of the Higher Edu-cation Sprout Project by the Ministry of Education(MOE), Taiwan. We thank Ms. Yu-Ching Kao,Mr. Nien-En Sun and the school badminton team,NCTU for assistance in data labeling, and AdvancedDatabase System Laboratory (ADSL Lab), NCTUin the work of tactical analysis.
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