References
- Andriluka, M., Pishchulin, L., Gehler, P., & Schiele, B. (2014). 2D human pose estimation: New benchmark and state of the art analysis. In IEEE Conference on Computer Vision and Pattern Recognition (cvpr), 3686–3693.
- Baysal, S., Kurt, M. C., & Duygulu, P. (2010). Recognizing human actions using key poses. In 20th International Conference on Pattern Recognition (ICPR), 1727–1730.
- Chu, X., Yang, W., Ouyang, W., Ma, C., Yuille, A. L., & Wang, X. (2017). Multicontext attention for human pose estimation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1831–1840.
- de Souza Vicente, C. M., Nascimento, E. R., Emery, L. E. C., Flor, C. A. G., Vieira, T., & Oliveira, L. B. (2016). High performance moves recognition and sequence segmentation based on key poses filtering. In IEEE Winter Conference on Applications of Computer Vision (WACV), 1–8.
- Einfalt, M., Zecha, D., & Lienhart, R. (2018). Activity-conditioned continuous human pose estimation for performance analysis of athletes using the example of swimming. In IEEE Winter Conference on Applications of Computer Vision (WACV), 446–455.
- Frey, B. J., & Dueck, D. (2007). Clustering by passing messages between data points. Science, 315 (5814), 972–976.
- Gorban, A., Idrees, H., Jiang, Y.-G., Roshan Zamir, A., Laptev, I., Shah, M., & Sukthankar, R. (2015). THUMOS challenge: Action recognition with a large number of classes. http://www.thumos.info/.
- Heilbron, F. C., Escorcia, V., Ghanem, B., & Niebles, J. C. (2015). Activitynet: A large-scale video benchmark for human activity understanding. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 961–970.
- Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady, 10, 707–710.
- Li, H., Tang, J., Wu, S., Zhang, Y., & Lin, S. (2010). Automatic detection and analysis of player action in moving background sports video sequences. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 20 (3), 351–364.
- Lv, F., & Nevatia, R. (2007). Single view human action recognition using key pose matching and viterbi path searching. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–8.
- Meyers, E. W. (1994). A sublinear algorithm for approximate keyword matching. Algorithmica, 12 (4-5), 345–374.
- Newell, A., Yang, K., & Deng, J. (2016). Stacked hourglass networks for human pose estimation. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), European Conference on Computer Vision (ECCV) (pp. 483–499). Cham: Springer International Publishing.
- Pansold, B., Zinner, J., & Gabriel, B. (1985). Zum einsatz und zur interpretation von laktatbestimmungen in der leistungsdiagnostik. Theorie und Praxis des Leistungssports, 23, 98–195.
- Pyne, D. B., Lee, H., & Swanwick, K. M. (2001). Monitoring the lactate threshold in world-ranked swimmers. Medicine and Science in Sports and Exercise, 33 (2), 291–297.
- Rabiner, L. R. (1989, Feb). A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77 (2), 257-286. doi: 10.1109/5.1862610.1109/5.18626
- Ren, C., Lei, X., & Zhang, G. (2011). Motion data retrieval from very large motion databases. In International Conference on Virtual Reality and Visualization (ICVRV), 70–77.
- Rowley, H. A., Baluja, S., & Kanade, T. (1998). Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (1), 23–38.
- Sedmidubsky, J., Valcik, J., & Zezula, P. (2013). A key-pose similarity algorithm for motion data retrieval. In 15th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS), 669–681.
- Victor, B., He, Z., Morgan, S., & Miniutti, D. (2017). Continuous video to simple signals for swimming stroke detection with convolutional neural networks. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 122–131.
- Vögele, A., Krüger, B., & Klein, R. (2014). Efficient unsupervised temporal segmentation of human motion. In Proceedings of the ACM Siggraph/Eurographics Symposium on Computer Animation, 167–176.
- Wang, C., Wang, Y., & Yuille, A. L. (2013). An approach to pose-based action recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 915–922.
- Wei, S.-E., Ramakrishna, V., Kanade, T., & Sheikh, Y. (2016). Convolutional pose machines. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4724–4732.
- Wu, C., Ma, Y.-F., Zhan, H.-J., & Zhong, Y.-Z. (2002). Events recognition by semantic inference for sports video. In IEEE International Conference on Multimedia and Expo (ICME), 1, 805–808.
- Yang, W., Li, S., Ouyang, W., Li, H., & Wang, X. (2017, Oct). Learning feature pyramids for human pose estimation. In IEEE International Conference on Computer Vision (ICCV).
- Zecha, D., Eggert, C., & Lienhart, R. (2017). Pose estimation for deriving kinematic parameters of competitive swimmers. In Computer Vision Applications in Sports, part of IS&T Electronic Imaging (pp. 21–29). Society for Imaging Science and Technology.