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Tracking Jockeys in a Cluttered Environment with Group Dynamics

Published:15 October 2019Publication History

ABSTRACT

This project aims to detect and track jockeys at the turning point of the horse races. The detection and tracking of the objects is a very challenging task in a crowded environment such as horse racing due to occlusion. However, in the horse race, the jockeys follow each other's paths and move as a slowly changing group. This group dynamic gives an important cue to approximate the location of obscured jockeys. This paper proposes a novel approach to handle occlusion by the integration of the group dynamic into jockeys tracking framework. The experimental result shows the effect of group dynamics on the tracking performance against partial and full occlusions.

References

  1. John G Allen, Richard YD Xu, and Jesse S Jin. 2004. Object tracking using camshift algorithm and multiple quantized feature spaces. In Proceedings of the Pan-Sydney area workshop on Visual information processing. Australian Computer Society, Inc., Darlinghurst, Australia, Australia, 3--7.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. M. Betke and Z. Wu. 2016. Data Association for Multi-Object Visual Tracking. Morgan & Claypool.Google ScholarGoogle Scholar
  3. Jean-Yves Bouguet. 2001. Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm. Intel Corporation, Microprocessor Research Labs 5, 1--10 (2001), 4.Google ScholarGoogle Scholar
  4. Yizheng Cai, Nando de Freitas, and James J Little. 2006. Robust visual tracking for multiple targets. In European conference on computer vision. Springer, Springer Berlin Heidelberg, Berlin, Heidelberg, 107--118.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Dorin Comaniciu and Peter Meer. 1999. Mean Shift Analysis and Applications. In Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2 (ICCV '99). IEEE Computer Society, Washington, DC, USA, 1197--. http://dl.acm.org/citation.cfm?id=850924.851593Google ScholarGoogle ScholarCross RefCross Ref
  6. Navneet Dalal and Bill Triggs. 2005. Histograms of Oriented Gradients for Human Detection. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01 (CVPR '05). IEEE Computer Society, Washington, DC, USA, 886--893. https: //doi.org/10.1109/CVPR.2005.177Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Bao Dang, An Tran, Tien Dinh, and Thang Dinh. 2010. A real time player tracking system for broadcast tennis video. In Asian Conference on Intelligent Information and Database Systems. Springer, Springer Berlin Heidelberg, Berlin, Heidelberg, 105--113.Google ScholarGoogle ScholarCross RefCross Ref
  8. Mandar Dixit and KS Venkatesh. 2009. Combining edge and color features for tracking partially occluded humans. In Asian Conference on Computer Vision. Springer Berlin Heidelberg, Berlin, Heidelberg, 140--149.Google ScholarGoogle Scholar
  9. Xingping Dong, Jianbing Shen, Dajiang Yu, Wenguan Wang, Jianhong Liu, and Hua Huang. 2017. Occlusion-aware real-time object tracking. IEEE Transactions on Multimedia 19, 4 (2017), 763--771.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Tiziana D'Orazio and Marco Leo. 2010. A review of vision-based systems for soccer video analysis. Pattern recognition 43, 8 (2010), 2911--2926. https://doi. org/10.1016/j.patcog.2010.03.009Google ScholarGoogle Scholar
  11. Weina Ge and Robert T Collins. 2008. Multi-target Data Association by Tracklets with Unsupervised Parameter Estimation. In In British Machine Vision Conference, Vol. 2. Citeseer, 5.Google ScholarGoogle ScholarCross RefCross Ref
  12. E. T. Hall. 1966. The Hidden Dimension: Man's Use of Space in Public and Private. The Bodley Head Ltd.Google ScholarGoogle Scholar
  13. Jungong Han, Dirk Farin, Peter H.N. de With, and Weilun Lao. 2005. Automatic Tracking Method for Sports Video Analysis. In 26th Symposium on Information Theory in the Benelux. 309--316.Google ScholarGoogle Scholar
  14. Sam Hare, Stuart Golodetz, Amir Saffari, Vibhav Vineet, Ming-Ming Cheng, Stephen L Hicks, and Philip HS Torr. 2016. Struck: Structured output tracking with kernels. IEEE transactions on pattern analysis and machine intelligence 38, 10 (2016), 2096--2109.Google ScholarGoogle Scholar
  15. Mohammad Hedayati, Michael J Cree, and Jonathan Scott. 2016. Scene structure analysis for sprint sports. In 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 1--5.Google ScholarGoogle ScholarCross RefCross Ref
  16. Dirk Helbing and Peter Molnar. 1995. Social force model for pedestrian dynamics. Physical review E 51, 5 (1995), 4282.Google ScholarGoogle Scholar
  17. David Held, Sebastian Thrun, and Silvio Savarese. 2016. Learning to track at 100 fps with deep regression networks. In European Conference on Computer Vision. Springer Berlin Heidelberg, Berlin, Heidelberg, 749--765.Google ScholarGoogle ScholarCross RefCross Ref
  18. João F Henriques, Rui Caseiro, Pedro Martins, and Jorge Batista. 2012. Exploiting the circulant structure of tracking-by-detection with kernels. In European conference on computer vision. Springer Berlin Heidelberg, Berlin, Heidelberg, 702--715.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. João F Henriques, Rui Caseiro, Pedro Martins, and Jorge Batista. 2015. High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 3 (2015), 583--596.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Zdenek Kalal, Krystian Mikolajczyk, and Jiri Matas. 2010. Forward-backward error: Automatic detection of tracking failures. In Pattern recognition (ICPR), 2010 20th international conference on. IEEE Computer Society, Washington, DC, USA, 2756--2759.Google ScholarGoogle Scholar
  21. Zdenek Kalal, Krystian Mikolajczyk, and Jiri Matas. 2012. Tracking-Learning- Detection. IEEE Trans. Pattern Anal. Mach. Intell. 34, 7 (July 2012), 1409--1422. https://doi.org/10.1109/TPAMI.2011.239Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Katta and Murty. 1968. An Algorithm for Ranking all the Assignments in Order of Increasing Cost. Operations Research 16, 3 (1968), 682--687.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Yiannis Kompatsiaris, Bernard Merialdo, and Shiguo Lian. 2012. TV content analysis: Techniques and applications. CRC Press.Google ScholarGoogle Scholar
  24. Baoxin Li, James H. Errico, Hao Pan, and Ibrahim Sezan. 2004. Bridging the semantic gap in sports video retrieval and summarization. Journal of Visual Communication and Image Representation 15, 3 (2004), 393--424.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Wei-Lwun Lu, J-A Ting, James J Little, and Kevin P Murphy. 2013. Learning to track and identify players from broadcast sports videos. Pattern Analysis and Machine Intelligence, IEEE Transactions on 35, 7 (2013), 1704--1716.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Wei-Lwun Lu, Jo-Anne Ting, James J Little, and Kevin P Murphy. 2013. Learning to track and identify players from broadcast sports videos. IEEE transactions on pattern analysis and machine intelligence 35, 7 (2013), 1704--1716.Google ScholarGoogle Scholar
  27. Wenhan Luo, Junliang Xing, Anton Milan, Xiaoqin Zhang, Wei Liu, Xiaowei Zhao, and Tae-Kyun Kim. 2014. Multiple object tracking: A literature review. arXiv preprint arXiv:1409.7618 (2014).Google ScholarGoogle Scholar
  28. Dr Emilio Maggio and Dr Andrea Cavallaro. 2011. Video Tracking: Theory and Practice (1st ed.). Wiley Publishing.Google ScholarGoogle ScholarCross RefCross Ref
  29. Anton Milan, Konrad Schindler, and Stefan Roth. 2013. Challenges of ground truth evaluation of multi-target tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 735--742.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Jiyan Pan and Bo Hu. 2007. Robust occlusion handling in object tracking. In 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, IEEE, Washington, DC, USA, 1--8.Google ScholarGoogle ScholarCross RefCross Ref
  31. Michael J Swain and Dana H Ballard. 1991. Color indexing. International journal of computer vision 7, 1 (1991), 11--32.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. John FA Taylor. 1958. The psychology of perception: A philosophical examination of Gestalt theory and derivative theories of perception. Vol. 55. 77--81 pages.Google ScholarGoogle Scholar
  33. Graham Thomas. 2011. Sports TV applications of computer vision. In Visual Analysis of Humans. Springer Berlin Heidelberg, Berlin, Heidelberg, 563--579.Google ScholarGoogle Scholar
  34. Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang. 2015. Object tracking benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 9 (2015), 1834-- 1848.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Alper Yilmaz, Omar Javed, and Mubarak Shah. 2006. Object Tracking: A Survey. ACM Comput. Surv. 38, 4, Article 13 (Dec. 2006).Google ScholarGoogle Scholar

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            • Published in

              cover image ACM Conferences
              MMSports '19: Proceedings Proceedings of the 2nd International Workshop on Multimedia Content Analysis in Sports
              October 2019
              120 pages
              ISBN:9781450369114
              DOI:10.1145/3347318

              Copyright © 2019 ACM

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              Publication History

              • Published: 15 October 2019

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