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A Systematic Review on Machine Learning-Based Sports Video Summarization Techniques

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Smart Computer Vision

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Abstract

Video summarization or highlights from long-hour sport video have been appreciated as one of the interesting and challenging techniques. Generally, the viewers of sports would be interested to have short summary of video. There are some interesting methods published in the literature addressing the issues on automatic sports video summarization. In this chapter, a systematic review on existing video summarization techniques is discussed by focusing on various algorithms and methods categorized under common ideas such as boundary shot detection, players/crowd/umpire shot classification and identification, key events detection, replay, strokes, commercials and play breaks-based detection, event, text, and excitement-based summarizations. The intention of the chapter is to recapitulate decades of development in sports video summarization for the benefit of the prospective researchers and exhibit future avenues to strengthen the outcome of video summarization techniques.

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References

  1. Rahman, A. A., Saleem, W., & Iyer, V. V. Driving behavior profiling and prediction in KSA using smart phone sensors and MLAs. In 2019 IEEE Jordan international joint conference on Electrical Engineering and Information Technology (JEEIT) (pp. 34–39).

    Google Scholar 

  2. Ajmal, M., Ashraf, M. H., Shakir, M., Abbas, Y., & Shah, F. A. (2012). Video summarization: Techniques and classification. In Computer vision and graphics (Vol. 7594). ISBN: 978-3-642-33563-1.

    Google Scholar 

  3. Sen, A., Deb, K., Dhar, P. K., & Koshiba, T. (2021). CricShotClassify: An approach to classifying batting shots from cricket videos using a convolutional neural network and gated recurrent unit. Sensors, 21, 2846. https://doi.org/10.3390/s21082846

    Article  Google Scholar 

  4. Halin, A. A., & Mandava, R. (2013, January). Goal event detection in soccer videos via collaborative multimodal analysis. Pertanika Journal of Science and Technology, 21(2), 423–442.

    Google Scholar 

  5. Amruta, A. D., & Kamde, P. M. (2015, March). Sports highlight generation system based on video feature extraction. IJRSI (2321–2705), II(III).

    Google Scholar 

  6. Bagheri-Khaligh, A., Raziperchikolaei, R., & Moghaddam, M. (2012). A new method for shot classification in soccer sports video based on SVM classifier. In Proceedings of the 2012 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). Santa Fe, NM.

    Google Scholar 

  7. Baijal, A., Jaeyoun, C., Woojung, L., & Byeong-Seob, K. (2015). Sports highlights generation based on acoustic events detection: A rugby case study. In 2015 IEEE International Conference on Consumer Electronics (ICCE) (pp. 20–23). https://doi.org/10.1109/ICCE.2015.7066303

  8. Alexey, B., Chien-Yao, W., & Hong-Yuan, M. L. (2020). YOLOv4: Optimal speed and accuracy of object detection. In arXiv 2004.10934[cs.CV].

    Google Scholar 

  9. Chen, F., De Vleeschouwer, C., Barrobés, H. D., Escalada, J. G., & Conejero, D. (2010). Automatic summarization of audio-visual soccer feeds. In 2010 IEEE international conference on Multimedia and Expo (pp. 837–842). https://doi.org/10.1109/ICME.2010.5582561

  10. Dai, J., Li, Y., He, K., & Sun, J. (2016). R-fcn: Object detection via region-based fully convolutional networks. In Advances in neural information processing systems (pp. 379–387).

    Google Scholar 

  11. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society conference on Computer Vision and Pattern Recognition (CVPR ‘05) (Vol. 1, pp. 886–893). https://doi.org/10.1109/CVPR.2005.177

  12. Jesse, D., & Mark, G. (2006). The relationship between Precision-Recall and ROC curves. In Proceedings of the 23rd International Conference on Machine Learning (ICML ‘06) (pp. 233–240). ACM, New York, NY, USA. https://doi.org/10.1145/1143844.1143874

  13. Asadi, E., & Charkari, N. M. (2012). Video summarization using fuzzy c-means clustering. In 20th Iranian conference on Electrical Engineering (ICEE2012) (pp. 690–694). https://doi.org/10.1109/IranianCEE.2012.6292442

  14. Ekin, A., Tekalp, A., & Mehrotra, R. (2003). Automatic soccer video analysis and summarization. IEEE Transactions on Image Processing, 12(7), 796–807.

    Article  Google Scholar 

  15. Fani, M., Yazdi, M., Clausi, D., & Wong, A. (2017). Soccer video structure analysis by parallel feature fusion network and hidden-to-observable transferring Markov model. IEEE Access, 5, 27322–27336.

    Article  Google Scholar 

  16. Felzenszwalb, P. F., Girshick, R. B., & McAllester, D. (2010). Cascade object detection with deformable part models. In 2010 IEEE computer society conference on Computer Vision and Pattern Recognition (pp. 2241–2248). https://doi.org/10.1109/CVPR.2010.5539906

  17. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010, September). Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 1627–1645. https://doi.org/10.1109/TPAMI.2009.167

    Article  Google Scholar 

  18. Felzenszwalb, P., McAllester, D., & Ramanan, D. (2008). A discriminatively trained, multiscale, deformable part model. In 2008 IEEE conference on Computer Vision and Pattern Recognition (pp. 1–8). https://doi.org/10.1109/CVPR.2008.4587597

  19. Foysal, M. F., Islam, M., Karim, A., & Neehal, N. (2018). Shot-Net: A convolutional neural network for classifying different cricket shots. In Recent trends in image processing and pattern recognition. Springer Singapore.

    Google Scholar 

  20. Ghanem, B., Kreidieh, M., Farra, M., & Zhang, T. (2012). Context-aware learning for automatic sports highlight recognition. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012) (pp. 1977–1980).

    Google Scholar 

  21. Girshick, R. B. (2012). From rigid templates to grammars: object detection with structured models (Ph.D. Dissertation). University of Chicago, USA. Advisor(s) Pedro F. Felzenszwalb. Order Number: AAI3513455.

    Google Scholar 

  22. Girshick, R. B., Felzenszwalb, P. F., & Mcallester, D. A. (2011). Object detection with grammar models. In Proceedings of the 24th international conference on Neural Information Processing Systems (NIPS’11) (pp. 442–450). Curran Associates Inc., Red Hook, NY, USA.

    Google Scholar 

  23. Girshick, R., & Fast, R.-C. N. N. (2015). 2015 IEEE International Conference on Computer Vision (ICCV) (pp. 1440–1448). https://doi.org/10.1109/ICCV.2015.169

    Book  Google Scholar 

  24. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016, January 1). Region-based convolutional networks for accurate object detection and segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(1), 142–158. https://doi.org/10.1109/TPAMI.2015.2437384

    Article  Google Scholar 

  25. Gonzalez, A., Bergasa, L., Yebes, J., & Bronte, S. (2012). Text location in complex images. In IEEE ICPR.

    Google Scholar 

  26. Gupta, A., & Muthaiah, S. (2020). Viewpoint constrained and unconstrained Cricket stroke localization from untrimmed videos. Image and Vision Computing, 100.

    Google Scholar 

  27. Gupta, A., & Muthaiah, S. (2019). Cricket stroke extraction: Towards creation of a large-scale cricket actions dataset. arXiv:1901.03107 [cs.CV].

    Google Scholar 

  28. Gupta, A., Karel, A., & Sakthi Balan, M. (2020). Discovering cricket stroke classes in trimmed telecast videos. In N. Nain, S. Vipparthi, & B. Raman (Eds.), Computer vision and image processing. CVIP 2019. Communications in computer and information science (Vol. 1148). Springer Singapore.

    Google Scholar 

  29. Arpan, G., Ashish, K., & Sakthi Balan, M. (2021). Cricket stroke recognition using hard and soft assignment based bag of visual words. In Communications in computer and information science (pp. 231–242). Springer Singapore. https://doi.org/10.1007/2F978-981-16-1092-2021

  30. Hari, R. (2015, November). Automatic summarization of hockey videos. IJARET (0976–6480), 6(11).

    Google Scholar 

  31. Harun-Ur-Rashid, M., Khatun, S., Trisha, Z., Neehal, N., & Hasan, M. (2018). Crick-net: A convolutional neural network based classification approach for detecting waist high no balls in cricket. arXiv preprint arXiv:1805.05974.

    Google Scholar 

  32. He, J., & Pao, H.-K. (2020). Multi-modal, multi-labeled sports highlight extraction. In 2020 international conference on Technologies and Applications of Artificial Intelligence (TAAI) (pp. 181–186). https://doi.org/10.1109/TAAI51410.2020.00041

  33. He, K., Zhang, X., Ren, S., & Sun, J. (2014). Spatial pyramid pooling in deep convolutional networks for visual recognition. In European conference on Computer Vision (pp. 346–361). Springer.

    Google Scholar 

  34. Khurram, I. M., Aun, I., & Nudrat, N. (2020). Automatic soccer video key event detection and summarization based on hybrid approach. Proceedings of the Pakistan Academy of Sciences, A Physical and Computational Sciences (2518–4245), 57(3), 19–30.

    Google Scholar 

  35. Islam, M. R., Paul, M., Antolovich, M., & Kabir, A. (2019). Sports highlights generation using decomposed audio information. In IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (pp. 579–584). https://doi.org/10.1109/ICMEW.2019.00105

  36. Islam, M., Hassan, T., & Khan, S. (2019). A CNN-based approach to classify cricket bowlers based on their bowling actions. In 2019 IEEE international conference on Signal Processing, Information, Communication & Systems (SPICSCON) (pp. 130–134). https://doi.org/10.1109/SPICSCON48833.2019.9065090

  37. Takahiro, I., Tsukasa, F., Shugo, Y., & Shigeo, M. (2017). Court-aware volleyball video summarization. In ACM SIGGRAPH 2017 posters (SIGGRAPH ‘17) (pp. 1–2). Association for Computing Machinery, New York, NY, USA, Article 74. https://doi.org/10.1145/3102163.3102204

  38. Javed, A., Malik, K. M., Irtaza, A., et al. (2020). A decision tree framework for shot classification of field sports videos. The Journal of Supercomputing, 76, 7242–7267. https://doi.org/10.1007/s11227-020-03155-8

    Article  Google Scholar 

  39. Javed, A., Bajwa, K., Malik, H., Irtaza, A., & Mahmood, M. (2016). A hybrid approach for summarization of cricket videos. In IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia). Seoul.

    Google Scholar 

  40. Javed, A., Irtaza, A., Khaliq, Y., & Malik, H. (2019). Replay and key-events detection for sports video summarization using confined elliptical local ternary patterns and extreme learning machine. Applied Intelligence, 49, 2899–2917. https://doi.org/10.1007/s10489-019-01410-x

    Article  Google Scholar 

  41. Jothi Shri, S., & Jothilakshmi, S. (2019). Crowd video event classification using convolutional neural network. Computer Communications, 147, 35–39.

    Article  Google Scholar 

  42. Kanade, S. S., & Patil, P. M. (2013, March). Dominant color based extraction of key frames for sports video summarization. International Journal of Advances in Engineering & Technology, 6(1), 504–512. ISSN: 2231-1963.

    Google Scholar 

  43. Kapela, R., McGuinness, K., & O’Connor, N. (2017). Real-time field sports scene classification using colour and frequency space decompositions. Journal of Real-Time Image Process, 13, 725–737.

    Article  Google Scholar 

  44. Kathirvel, P., Manikandan, S. M., & Soman, K. P. (2011, January). Automated referee whistle sound detection for extraction of highlights from sports video. International Journal of Computer Applications (0975–8887), 12(11), 16–21.

    Article  Google Scholar 

  45. Khan, A., Shao, J., Ali, W., & Tumrani, S. (2020). Content-aware summarization of broadcast sports videos: An audio–visual feature extraction approach. Neural Process Letter, 1945–1968.

    Google Scholar 

  46. Kiani, V., & Pourreza, H. R. (2013). Flexible soccer video summarization in compressed domain. In ICCKE 2013 (pp. 213–218). https://doi.org/10.1109/ICCKE.2013.6682798

  47. Kolekar, M. H., & Sengupta, S. (2015). Bayesian network-based customized highlight generation for broadcast soccer videos. IEEE Transactions on Broadcasting, (2), 195–209.

    Google Scholar 

  48. Kolekar, M. H., & Sengupta, S. (2006). Event-importance based customized and automatic cricket highlight generation. In IEEE international conference on Multimedia and Expo. Toronto, ON.

    Google Scholar 

  49. Kolekar, M. H., & Sengupta, S. (2008). Caption content analysis based automated cricket highlight generation. In National Communications Conference (NCC). Mumbai.

    Google Scholar 

  50. Bhattacharya, K., Chaudhury, S., & Basak, J. (2004, December 16–18). Video summarization: A machine learning based approach. In ICVGIP 2004, Proceedings of the fourth Indian conference on Computer Vision, Graphics & Image Processing (pp. 429–434). Allied Publishers Private Limited, Kolkata, India.

    Google Scholar 

  51. Alex, K., Ilya, S., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th international conference on Neural Information Processing Systems, Volume 1 (NIPS’12) (pp. 1097–1105). Curran Associates Inc., Red Hook, NY, USA.

    Google Scholar 

  52. Kumar, R., Santhadevi, D., & Janet, B. (2019). Outcome classification in cricket using deep learning. In IEEE international conference on Cloud Computing in Emerging Markets CCEM. Bengaluru.

    Google Scholar 

  53. Kumar Susheel, K., Shitala, P., Santosh, B., & Bhaskar, S. V. (2010). Sports video summarization using priority curve algorithm. International Journal on Computer Science and Engineering (0975–3397), 02(09), 2996–3002.

    Google Scholar 

  54. Kumar, Y., Gupta, S., Kiran, B., Ramakrishnan, K., & Bhattacharyya, C. (2011). Automatic summarization of broadcast cricket videos. In IEEE 15th International Symposium on Consumer Electronics (ISCE). Singapore.

    Google Scholar 

  55. Li, Y., Chen, Y., Wang, N., & Zhang, Z. (2019). Scale-aware trident networks for object detection. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 6053–6062). https://doi.org/10.1109/ICCV.2019.00615

  56. Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., & Sun, J. (2017). Light-head r-cnn: In defense of two-stage object detector. arXiv preprint arXiv:1711.07264.

    Google Scholar 

  57. Lin, T., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In IEEE conference on Computer Vision and Pattern Recognition (CVPR) (pp. 936–944). https://doi.org/10.1109/CVPR.2017.106

  58. Lin, T., Goyal, P., Girshick, R., He, K., & Dollár, P. (2018, July). Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2), 318–327. https://doi.org/10.1109/TPAMI.2018.2858826

    Article  Google Scholar 

  59. Merler, M., Mac, K. N. C., Joshi, D., Nguyen, Q. B., Hammer, S., Kent, J., Xiong, J., Do, M. N., Smith, J. R., & Feris, R. S. (2019, May). Cricket automatic curation of sports highlights using multimodal excitement features. IEEE Transactions on Multimedia, 21(5), 1147–1160. https://doi.org/10.1109/TMM.2018.2876046

    Article  Google Scholar 

  60. Minhas, R., Javed, A., Irtaza, A., Mahmood, M., & Joo, Y. (2019). Shot classification of field sports videos using AlexNet Convolutional Neural Network. Applied Sciences, 9(3), 483.

    Article  Google Scholar 

  61. Mohan, S., & Vani, V. (2016). Predictive 3D content streaming based on decision tree classifier approach. In S. Satapathy, J. Mandal, S. Udgata, & V. Bhateja (Eds.), Information systems design and intelligent applications. Advances in intelligent systems and computing (Vol. 433). Springer. https://doi.org/10.1007/978-81-322-2755-7_16

  62. Namuduri, K. (2009). Automatic extraction of highlights from a cricket video using MPEG-7 descriptors. In First international communication systems and networks and workshops. Bangalore.

    Google Scholar 

  63. Nguyen, N., & Yoshitaka, A. (2014). Soccer video summarization based on cinematography and motion analysis. In 2014 IEEE 16th international workshop on Multimedia Signal Processing (MMSP) (pp. 1–6). https://doi.org/10.1109/MMSP.2014.6958804

  64. Rafiq, M., Rafiq, G., Agyeman, R., Choi, G., & Jin, S.-I. (2020). Scene classification for sports video summarization using transfer learning. Sensors, 20, 1702.

    Article  Google Scholar 

  65. Raj, R., Bhatnagar, V., Singh, A. K., Mane, S., & Walde, N. (2019, May). Video summarization: Study of various techniques. In Proceedings of IRAJ international conference, arXiv:2101.08434.

    Google Scholar 

  66. Raventos, A., Quijada, R., Torres, L., & Tarrés, F. (2015). Automatic summarization of soccer highlights using audio-visual descriptors. Springer Plus, 4, 1–13.

    Article  Google Scholar 

  67. Ravi, A., Venugopal, H., Paul, S., & Tizhoosh, H. R. (2018). A dataset and preliminary results for umpire pose detection using SVM classification of deep features. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1396–1402). https://doi.org/10.1109/SSCI.2018.8628877

  68. Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. In 2017 IEEE conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6517–6525). https://doi.org/10.1109/CVPR.2017.690

  69. Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.

    Google Scholar 

  70. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (pp. 779–788).

    Google Scholar 

  71. Ren, S., He, K., Girshick, R., & Sun, J. (2016). Faster R-CNN: Towards real-time object detection with region proposal. arXiv:1506.01497 [cs.CV].

    Google Scholar 

  72. Sharma, R., Sankar, K., & Jawahar, C. (2015). Fine-grain annotation of cricket videos. In Proceedings of the 3rd IAPR Asian Conference on Pattern Recognition (ACPR). Kuala Lumpur, Malaysia.

    Google Scholar 

  73. Shih, H. (2018). A survey of content-aware video analysis for sports. IEEE Transactions on Circuits and Systems for Video Technology, 28(5), 1212–1231.

    Article  Google Scholar 

  74. Shingrakhia, H., & Patel, H. (2021). SGRNN-AM and HRF-DBN: A hybrid machine learning model for cricket video summarization. The Visual Computer, 38, 2285. https://doi.org/10.1007/s00371-021-02111-8

    Article  Google Scholar 

  75. Shukla, P., Sadana, H., Verma, D., Elmadjian, C., Ramana, B., & Turk, M. (2018). Automatic cricket highlight generation using event-driven and excitement-based features. In IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Salt Lake City, UT.

    Google Scholar 

  76. Sreeja, M. U., & KovoorBinsu, C. (2019). Towards genre-specific frameworks for video summarisation: A survey. Journal of Visual Communication and Image Representation (1047–3203), 62, 340–358. https://doi.org/10.1016/j.jvcir.2019.06.004

    Article  Google Scholar 

  77. Su Yuting., Wang Weikang., Liu Jing., Jing Peiguang., and Yang Xiaokang., DS-Net: Dynamic spatiotemporal network for video salient object detection, arXiv:2012.04886 [cs.CV], 2020.

    Google Scholar 

  78. Sukhwani, M., & Kothari, R. A parameterized approach to personalized variable length summarization of soccer matches. arXiv preprint arXiv:1706.09193.

    Google Scholar 

  79. Sun, Y., Ou, Z., Hu, W., & Zhang, Y. (2010). Excited commentator speech detection with unsupervised model adaptation for soccer highlight extraction. In 2010 international conference on Audio, Language, and Image Processing (pp. 747–751). https://doi.org/10.1109/ICALIP.2010.5685077

  80. Tang, H., Kwatra, V., Sargin, M., & Gargi, U. (2011). Detecting highlights in sports videos: Cricket as a test case. In IEEE international conference on Multimedia and Expo. Barcelona.

    Google Scholar 

  81. Saba, T., & Altameem, A. (2013, August). Analysis of vision based systems to detect real time goal events in soccer videos. International Journal of Applied Artificial Intelligence, 27(7), 656–667. https://doi.org/10.1080/08839514.2013.787779

    Article  Google Scholar 

  82. Antonio, T.-d.-P., Yuta, N., Tomokazu, S., Naokazu, Y., Marko, L., & Esa, R. (2018, August). Summarization of user-generated sports video by using deep action recognition features. IEEE Transactions on Multimedia, 20(8), 2000–2010.

    Article  Google Scholar 

  83. Tien, M.-C., Chen, H.-T., Hsiao, C. Y.-W. M.-H., & Lee, S.-Y. (2007). Shot classification of basketball videos and its application in shooting position extraction. In Proceedings of the IEEE international conference on Acoustics, Speech and Signal Processing (ICASSP 2007).

    Google Scholar 

  84. Vadhanam, B. R. J., Mohan, S., Ramalingam, V., & Sugumaran, V. (2016). Performance comparison of various decision tree algorithms for classification of advertisement and non-advertisement videos. Indian Journal of Science and Technology, 9(1), 48–65.

    Google Scholar 

  85. Vani, V., Kumar, R. P., & Mohan, S. Profiling user interactions of 3D complex meshes for predictive streaming and rendering. In Proceedings of the fourth international conference on Signal and Image Processing 2012 (ICSIP 2012) (pp. 457–467). Springer, India.

    Google Scholar 

  86. Vani, V., & Mohan, S. (2021). Advances in sports video summarization – a review based on cricket video. In The 34th international conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems, Special Session on Big Data and Intelligence Fusion Analytics (BDIFA 2021). Accepted for publication in Springer LNCS.

    Google Scholar 

  87. Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society conference on Computer Vision and Pattern Recognition. CVPR 2001 (p. I-I). https://doi.org/10.1109/CVPR.2001.990517

  88. Viola, P., & Jones, M. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154.

    Article  Google Scholar 

  89. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. In European conference on computer vision (pp. 21–37). Springer.

    Google Scholar 

  90. Xu, W., & Yi, Y. (2011, September). A robust replay detection algorithm for soccer video. IEEE Signal Processing Letters, 18(9), 509–512. https://doi.org/10.1109/LSP.2011.2161287

    Article  Google Scholar 

  91. Khan, Y. S., & Pawar, S. (2015). Video summarization: Survey on event detection and summarization in soccer videos. International Journal of Advanced Computer Science and Applications (IJACSA), 6(11). https://doi.org/10.14569/IJACSA.2015.061133

  92. Ye, J., Kobayashi, T., & Higuchi, T. Audio-based sports highlight detection by Fourier local auto-correlations. In Proceedings of the 11th annual conference of the International Speech Communication Association, INTERSPEECH 2010 (pp. 2198–2201).

    Google Scholar 

  93. Hossam, Z. M., Nashwa, E.-B., Ella, H. A., & Tai-hoon, K. (2011). Machine learning-based soccer video summarization system, multimedia, computer graphics and broadcasting (Vol. 263). ISBN: 978-3-642-27185-4.

    Google Scholar 

  94. Zhang, S., Wen, L., Bian, X., Lei, Z., & Li, S. Z. (2018). Singleshot refinement neural network for object detection. In IEEE CVPR.

    Google Scholar 

  95. Zhang, S., Wen, L., Lei, Z., & Li, S. Z. (2021, February). RefineDet++: Single-shot refinement neural network for object detection. IEEE Transactions on Circuits and Systems for Video Technology, 31(2), 674–687. https://doi.org/10.1109/TCSVT.2020.2986402

    Article  Google Scholar 

  96. Zou, Z., Shi, Z., Guo, Y., & Ye, J. (2019). Object detection in 20 years: A survey. arXiv preprint arXiv:1905.05055.

    Google Scholar 

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Authors and Affiliations

  1. Department of CSE, Nitte Meenakshi Institute of Technology, Bengaluru, India

    Vani Vasudevan

  2. Department of ISE, Nitte Meenakshi Institute of Technology, Bengaluru, India

    Mohan S. Gounder

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  1. PSG College of Technology, Coimbatore, Tamil Nadu, India

    B. Vinoth Kumar

  2. Thiruvettakudy, National Institute of Technology, Puducherry, Karaikal, India

    P. Sivakumar

  3. Thiruvettakudy, National Institute of Technology, Puducherry, Karaikal, India

    B. Surendiran

  4. University of North Texas, Denton, TX, USA

    Junhua Ding

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Vasudevan, V., Gounder, M.S. (2023). A Systematic Review on Machine Learning-Based Sports Video Summarization Techniques. In: Kumar, B.V., Sivakumar, P., Surendiran, B., Ding, J. (eds) Smart Computer Vision. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-20541-5_1

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