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Automatic analysis of complex athlete techniques in broadcast taekwondo video

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Abstract

Athlete detection and action recognition in sports video is a very challenging task due to the dynamic and cluttered background. Several attempts for automatic analysis focus on athletes in many sports videos have been made. However, taekwondo video analysis remains an unstudied field. In light of this, a novel framework for automatic techniques analysis in broadcast taekwondo video is proposed in this paper. For an input video, in the first stage, athlete tracking and body segmentation are done through a modified Structure Preserving Object Tracker. In the second stage, the de-noised frames which completely contain the body of analyzed athlete from video sequence, are trained by a deep learning network PCANet to predict the athlete action of each single frame. As one technique is composed of many consecutive actions and each action corresponds a video frame, focusing on video sequences to achieve techniques analysis makes sense. In the last stage, linear SVM is used with the predicted action frames to get a techniques classifier. To evaluate the performance of the proposed framework, extensive experiments on real broadcast taekwondo video dataset are provided. The results show that the proposed method achieves state-of-the-art results for complex techniques analysis in taekwondo video.

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References

  1. Afrouzian R, Seyedarabi H, Kasaei S (2016) Pose estimation of soccer players using multiple uncalibrated cameras. Multimedia Tools and Applications 75:6809–6827

    Article  Google Scholar 

  2. Archana M, Kalaiselvi Geetha M (2016) An efficient ball and player detection in broadcast tennis video. Intell Syst Technol Appl 384:427–436

    Google Scholar 

  3. Chan TH, Jia K, Gao S et al (2015) PCANet: a simple deep learning baseline for image classification? IEEE Trans Image Process 24:5017–5032

    Article  MathSciNet  Google Scholar 

  4. Chen CM, Chen LH (2014) A novel method for slow motion replay detection in broadcast basketball video. Multimedia Tools and Applications 74:9573–9593

    Article  Google Scholar 

  5. Chu WT, Wu JL (2008) Explicit semantic events detection and development of realistic applications for broadcasting baseball videos. Multimedia Tools and Applications 38:27–50

    Article  Google Scholar 

  6. Crammer K, Dekel O, Keshet J et al (2006) Online passive-aggressive algorithms. J Mach Learn Res 7:551–585

    MathSciNet  MATH  Google Scholar 

  7. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. Proceedings – 2005. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 I:886–893

  8. Dao MS, Babaguchi N (2010) A new spatio-temporal method for event detection and personalized retrieval of sports video. Multimedia Tools and Applications 50:227–248

    Article  Google Scholar 

  9. Duh D-J, Chang S-Y, Chen S-Y, Kan C-C (2013) Automatic broadcast soccer video analysis, player detection, and tracking based on color histogram. Intelligent Technologies and Engineering Systems. Springer New York, New York, pp 123–130

  10. Everts I, van Gemert JC, Gevers T (2013) Evaluation of color STIPs for human action recognition. IEEE Conf Comput Vis Pattern Recognit 23:2850–2857

    Google Scholar 

  11. Fan R-E, Chang K-W, Hsieh C-J et al (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9:1871–1874

    MATH  Google Scholar 

  12. Farajidavar N, Campos T D, Kittler J, et al (2012) Transductive transfer learning for action recognition in tennis games. IEEE International Conference on Computer Vision Workshops: 1548–1553

  13. Fathi A, Mori G (2008) Action recognition by mid-level motion features. IEEE International Conference on Computer Vision and Pattern Recognition: 1–8

  14. Feichtenhofer C, Pinz A, Zisserman A (2016) Convolutional two-stream network fusion for video action recognition. IEEE Conf Comput Vis Pattern Recognit 2016:1933–1941

    Google Scholar 

  15. Felzenszwalb PF, Girshick RB, McAllester D et al (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32:1627–1645

    Article  Google Scholar 

  16. Gastin PB, McLean O, Spittle M, Breed RVP (2013) Quantification of tackling demands in professional Australian football using integrated wearable athlete tracking technology. J Sci Med Sport 16:589–593

    Article  Google Scholar 

  17. Ghasemzadeh H, Jafari R (2011) Coordination analysis of human movements with body sensor networks: a signal processing model to evaluate baseball swings. IEEE Sensors J 11:603–610

    Article  Google Scholar 

  18. Gouwanda D, Senanayake SMNA (2008) Emerging trends of body-mounted sensors in sports and human gait analysis. 4th Kuala Lumpur International Conference on Biomedical Engineering :715–718

  19. Kumar K, Prasad S (2010) Sports video summarization using priority curve algorithm. Int J Comput Sci Eng 2:2996–3002

    Google Scholar 

  20. Li H, Lin S, Zhang Y, Tao K (2007) Automatic video-based analysis of athlete action. Proceedings of 14th International conference on Image Analysis and Processing, ICIAP 2007 205–210

  21. Li H, Tang J, Wu S et al (2010) Automatic detection and analysis of player action in moving background sports video sequences. IEEE Trans Circuits Syst Video Technol 20:351–364

    Article  Google Scholar 

  22. Liu J, Carr P, Collins RT, Liu Y (2013) Tracking sports players with context-conditioned motion models. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1830–1837

  23. Ma S, Zhang J, Ikizler-Cinbis N, Sclaroff S (2013) Action Recognition and Localization by Hierarchical Space-Time Segments. Proceedings of the IEEE International Conference on Computer Vision, ICCV 2013:2744–2751

    Google Scholar 

  24. Mei T, Hua XS (2008) Structure and event mining in sports video with efficient mosaic. Multimedia Tools and Applications 40:89–110

    Article  Google Scholar 

  25. Mendi E, Clemente HB, Bayrak C (2013) Sports video summarization based on motion analysis. Comput Electr Eng 39:790–796

    Article  Google Scholar 

  26. Nitta N, Babaguchi N, Kitahashi T (2005) Generating semantic descriptions of broadcasted sports videos based on structures of sports games and TV programs. Multimedia Tools and Applications 25:59–83

    Article  Google Scholar 

  27. Qian X, Wang H, Liu G, Hou X (2012) HMM based soccer video event detection using enhanced mid-level semantic. Multimedia Tools and Applications 60:233–255

    Article  Google Scholar 

  28. Roh MC, Christmas B, Kittler J, Lee SW (2008) Gesture spotting for low-resolution sports video annotation. Pattern Recogn 41:1124–1137

    Article  MATH  Google Scholar 

  29. Simonyan K, Zisserman A (2015) Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of International Conference on Learning Representations ICRL 2015 1–14

  30. Tao W, Liu T, Zheng R, Feng H (2012) Gait analysis using wearable sensors. Sensors 12:2255–2283

    Article  Google Scholar 

  31. Tsochantaridis I, Joachims T, Hofmann T et al (2005) Large margin methods for structured and interdependent output variables. J Mach Learn Res 6:1453–1484

    MathSciNet  MATH  Google Scholar 

  32. Vainstein J, Delrieux C, Maguitman A (2013) Action recognition in tennis videos using optical flow and conditional random fields. Argentine Symposium on Technology, AST 2013:152–162

    Google Scholar 

  33. Wang Z, Yu J, He Y, Guan T (2014) Affection arousal based highlight extraction for soccer video. Multimedia Tools and Applications 73:519–546

    Article  Google Scholar 

  34. Xie X, Zaitsev Y, Velásquez-García LF et al (2014) Scalable, MEMS-enabled, vibrational tactile actuators for high resolution tactile displays. J Micromech Microeng 24:125014

    Article  Google Scholar 

  35. Xie X, Zaitsev Y, Velásquez-García LF, et al (2014) Compact, scalable, high-resolution, MEMS-enabled tactile displays. Proceedings of Solid-State Sensors, Actuators, and Microsystems Workshop 127–130

  36. Xing J, Ai H, Liu L, Lao S (2011) Multiple player tracking in sports video: a dual-mode two-way Bayesian inference approach with progressive observation modeling. IEEE Trans Image Process 20:1652–1667

    Article  MathSciNet  MATH  Google Scholar 

  37. Xu C, Wang J, Lu H, Zhang Y (2008) A novel framework for semantic annotation and personalized retrieval of sports video. IEEE Trans Multimedia 10:421–436

    Article  Google Scholar 

  38. Zawbaa HM, El-Bendary N, Hassanien AE, Kim TH (2011) Machine learning-based soccer video summarization system. Communications in Computer and Information Science CCIS 263:19–28

    Article  Google Scholar 

  39. Zhang L, Van Der Maaten L (2013) Structure preserving object tracking. Proc IEEE Conf Comput Vis Pattern Recognit 2013:1838–1845

    Google Scholar 

  40. Zhang L, Van Der Maaten L (2014) Preserving structure in model-free tracking. IEEE Trans Pattern Anal Mach Intell 36:756–769

    Article  Google Scholar 

  41. Zhen X, Shao L, Tao D, Li X (2013) Embedding motion and structure features for action recognition. IEEE Trans Circuits Syst Video Technol 23:1182–1190

    Article  Google Scholar 

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Authors and Affiliations

  1. Ocean University of China, Qingdao, China

    Yongqiang Kong, Zhengang Wei & Shanshan Huang

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  1. Yongqiang Kong
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  2. Zhengang Wei
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  3. Shanshan Huang
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Correspondence to Zhengang Wei.

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Kong, Y., Wei, Z. & Huang, S. Automatic analysis of complex athlete techniques in broadcast taekwondo video. Multimed Tools Appl 77, 13643–13660 (2018). https://doi.org/10.1007/s11042-017-4979-0

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  • DOI: https://doi.org/10.1007/s11042-017-4979-0

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