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Semi-automatic Basketball Jump Shot Annotation Using Multi-view Activity Recognition and Deep Learning

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HCI International 2023 Posters (HCII 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1836))

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Abstract

Statisticians introduce subjectivity while recording game statistics. This research aims to reduce this problem using a multi-view pose classification model, starting with the jump shot location event annotation. Basketball simulations will be conducted to determine if the proposed model can be more objective than a human statistician recording the same jump shot event. To this end, the Exhaustive Basketball System (EBS) was developed. EBS is a web application that allows customizable courts and game rules variations, enabling storing in-game event data. Allowing the extraction of the necessary jump shot coordinates data recorded by the statistician during the simulations for analyses. By controlling the number of players, game time duration, and an agility index grouping technique proposed for the basketball simulations, their impact on the coordinates data will be analyzed in an ANOVA 3*2*3 factorial design with three repetitions. The response variable is the average distance of the jump shot attempts event annotation regarding the ground truth location. While other researchers have worked on jump shot recognition using a single view of the court, our research attempts to contribute to this concept but with multiple synchronized viewing angles in addition to subjectivity reduction. We expect to prove an ideal game statistics generation technique to register objective statistics. Moreover, be a pathway for objective game statistics and present recommendations for future work related to other sports or fields that could benefit from the proposed technique.

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References

  1. Bruch, H., Hahn, A.G., Helmer, R.J., MacKintosh, C., Blanchonette, I., McKenna, M.J.: Evaluation of an automated scoring system in a modified form of competitive boxing. Proc. Eng. 13, 445–450 (2011). https://doi.org/10.1016/j.proeng.2011.05.112

    Article  Google Scholar 

  2. Cao, C.: Sports data mining technology used in basketball outcome prediction (Doctoral dissertation) (2012)

    Google Scholar 

  3. Cao, Z., Hidalgo Martinez, G., Simon, T., Wei, S., Sheikh, Y.A.: Openpose: realtime multi-person 2d pose estimation using part affinity fields. IEEE Trans. Patt. Anal. Mach. Intell. (2019)

    Google Scholar 

  4. Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y.: Realtime multi-person 2d pose estimation using part affinity fields. In: Cvpr (2017)

    Google Scholar 

  5. Chen, L. H., Chang, H.W., Hsiao, H.A.: Player trajectory reconstruction from broadcast basketball video. In: ACM International Conference Proceeding Series, pp. 72–76 (2017). https://doi.org/10.1145/3133793.3133801

  6. Figueira, B., Gonçalves, B., Folgado, H., Masiulis, N., Calleja-González, J., Sampaio, J.: Accuracy of a basketball indoor tracking system based on standard bluetooth low energy channels (NBN23®). Sensors (Switzerland) 18(6), 2–9 (2018). https://doi.org/10.3390/s18061940

    Article  Google Scholar 

  7. Hahn, A.G., et al.: Development of an automated scoring system for amateur boxing. Proc. Eng. 2(2), 3095–3101 (2010). https://doi.org/10.1016/j.proeng.2010.04.117

    Article  Google Scholar 

  8. Huang, C.-L., Shih, H.-C., Chen, C.-L.: Shot and scoring events identification of basketball videos. In: 2006 IEEE International Conference on Multimedia and Expo, pp. 9–12, July 2006. https://doi.org/10.1109/ICME.2006.262923

  9. Ji, R.: Research on basketball shooting action based on image feature extraction and machine. Learning 8, 138743–138751 (2020). https://doi.org/10.1109/ACCESS.2020.3012456

    Article  Google Scholar 

  10. Johnson, N.: Extracting player tracking data from video using non-stationary cameras and a combination of computer vision techniques. In: MIT Sloan Sports Analytics Conference, pp. 1–14 (2020)

    Google Scholar 

  11. Lin, T., Yang, Y., Beyer, J., Pfister, H.: SportsXR - immersive analytics in sports. In: CHI 2020: CHI Conference on Human Factors in Computing Systems, pp. 25–30 (2020). https://doi.org/10.1145/3334480. arXiv 2004.08010

  12. Meng, W., Xu, S., Li, E., Zeng, X., Zhang, X.: Accurate 3D locating and tracking of basketball players from multiple videos. In: SIGGRAPH Asia 2018 Technical Briefs, SA 2018 (2018). https://doi.org/10.1145/3283254.3283265

  13. Monier, E., Wilhelm, P., Rückert, U.: A Computer vision based tracking system for indoor team sports. In: The fourth International Conference on Intelligent Computing and Information Systems (2009)

    Google Scholar 

  14. Nguyen, L.N.N., Rodríguez-Martín, D., Català, A., Pérez-López, C., Samà, A., Cavallaro, A.: Basketball activity recognition using wearable inertial measurement units. In: ACM International Conference Proceeding Series, 07–09 September (2015). https://doi.org/10.1145/2829875.2829930

  15. Simon, T., Joo, H., Matthews, I., Sheikh, Y.: Hand keypoint detection in single images using multiview bootstrapping. In: Cvpr (2017)

    Google Scholar 

  16. Tanikawa, S., Tagawa, N.: Player Tracking using Multi-viewpoint Images in Basketball Analysis. In: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, (VISI-GRAPP), pp. 813–820 (2020). https://doi.org/10.5220/0009097408130820

  17. Thomas, G., Gade, R., Moeslund, T.B., Carr, P., Hilton, A.: Computer vision for sports: current applications and research topics. Comput. Vis. Image Underst. 159, 3–18 (2017). https://doi.org/10.1016/j.cviu.2017.04.011

    Article  Google Scholar 

  18. Tichy, W.: Changing the game: “Dr. Dave” Schrader on sports analytics. Ubiquity 2016, Article 1, pp. 1–10, May 2016. https://doi.org/10.1145/2933230

  19. Tien, M.-c., Chen, H.-t., Chen, Y.-w., Hsiao, M.-h., Lee, S.-y.: Shot classification of basketball videos and its application in shooting position extraction. In: IEEE International Conference on Acoustics, Speech and Signal Processing – ICASSP 2007 (2007). https://doi.org/10.1109/ICASSP.2007.366100

  20. Pauole, K., Madole, K., Garhammer, J., Lacourse, M., Rozenek, R.: Reliability and validity of the T-Test as a measure of agility, leg power, and leg speed in college-aged men and women. J. Strength Cond. Res. 14(4), 443–450 (2000)

    Google Scholar 

  21. Perin, C., Vuillemot, R., Stolper, C.D., Stasko, J.T., Wood, J., Carpendale, S.: State of the art of sports data visualization. Comput. Graph. Forum 37(3), 663–686 (2018). https://doi.org/10.1111/cgf.13447

    Article  Google Scholar 

  22. Ratgeber, L., Ivankovic, Z., Gojkovic, Z., Milosevic, Z., Markoski, B., Kostic-Zobenica, A.: Video mining in basketball shot and game analysis. In: Acta Polytechnica Hungarica, vol. 16, no. 1, pp. 7–27 (2019). https://doi.org/10.12700/APH.16.1.2019.1.1

  23. van Bommel, M., Bornn, L.: The van excel effect: adjusting for scorekeeper bias in NBA box scores. In: MIT Sloan Sports Analytics Conference, pp. 1–15 (2016)

    Google Scholar 

  24. van Bommel, M., Bornn, L.: Adjusting for scorekeeper bias in NBA box scores. Data Min. Knowl. Disc. 31(6), 1622–1642 (2017). https://doi.org/10.1007/s10618-017-0497-y

    Article  MathSciNet  Google Scholar 

  25. van Bommel, M., Bornn, L., Chow-White, P., Gao, C.: Home sweet home: quantifying home court advantages for NCAA basketball statistics, pp. 1–13 (2019). https://doi.org/10.48550/arXiv.1909.04817. arXiv 1909.04817

  26. Vleeschouwer, C., et al.: Distributed video acquisition and annotation for sport-event summarization (2008)

    Google Scholar 

  27. Wang, K.-C., & Zemel, R.: Classifying NBA Offensive Plays Using Neural Networks. MIT Sloan Sports Analytics Conference, 1–9. (2016),

    Google Scholar 

  28. Wei, S.-E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: Cvpr (2016)

    Google Scholar 

  29. Wen, P.-C., Cheng, W.-C., Wang, Y.-S., Chu, H.-K., Tang, N.C., Liao, H.-Y.M.: Court reconstruction for camera calibration in broadcast basketball videos. IEEE Trans. Visual Comput. Graphics 22(5), 1517–1526 (2015). https://doi.org/10.1109/TVCG.2015.2440236

    Article  Google Scholar 

  30. Zhang, S., Wei, Z., Nie, J., Huang, L., Wang, S., Li, Z.: A review on human activity recognition using vision-based method. J. Healthc. Eng. (2017). https://doi.org/10.1155/2017/3090343

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Authors and Affiliations

  1. Polytechnic University of Puerto Rico, 00918, San Juan, Puerto Rico

    Samuel E Matos Flores

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  1. Samuel E Matos Flores
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Correspondence to Samuel E Matos Flores .

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Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology - Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. Foundation for Research and Technology - Hellas (FORTH), Heraklion, Crete, Greece

    Margherita Antona

  3. Foundation for Research and Technology - Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

  4. University of Central Florida, Orlando, FL, USA

    Gavriel Salvendy

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Matos Flores, S.E. (2023). Semi-automatic Basketball Jump Shot Annotation Using Multi-view Activity Recognition and Deep Learning. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2023 Posters. HCII 2023. Communications in Computer and Information Science, vol 1836. Springer, Cham. https://doi.org/10.1007/978-3-031-36004-6_66

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  • DOI: https://doi.org/10.1007/978-3-031-36004-6_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36003-9

  • Online ISBN: 978-3-031-36004-6

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