참고문헌
- J. Gao, Z. Yang, and R. Nevatia, "Red: Reinforced encoderdecoder networks for action anticipation," in Proc. Bri. Mach. Vis. Conf. (BMVC), London, UK, Sept. 2017, pp. 92.1-92.11.
- M. Xu et al., "Temporal recurrent networks for online action detection," in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Seoul, Rep. of Korea, Oct. 2019, pp. 5532-5541.
- H. Eun et al., "Learning to discrimiate information for online action detection," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. (CVPR), Seattle, WA, USA, June 2020, pp. 806-815.
- H. Eun et al., "Temporal filtering networks for online action detection," Pattern Recognit. (PR), vol. 111, Mar. 2021.
- R. De Geest et al., "Online action detection," in Proc. Eur. Conf. Comput. Vis. (ECCV), Glasgow, UK, Oct. 2016, pp. 269-285.
- Y.-G. Jiang et al., "Challenge: Action recognition with a large number of classes," ECCV'14 THUMOS, 2014, http://crcv.ucf.edu/THUMOS14/
- L. Wang et al., "Temporal segment networks: Towards good practices for deep action recognition," in Proc. Eur. Conf. Comput. Vis. (ECCV), Amsterdam, Netherlands, Oct. 2016, pp. 20-36.
- R. De Geest and T. Tuytelaars, "Modeling temporal structure with lstm for online action detection," in Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV), Lake Tahoe, NV, USA, Mar. 2018, pp. 1549-1557.
- F. C. Heilbron et al., "ActivityNet: A large-scale video benchmark for human activity understanding," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. (CVPR), Boston, MA, USA, June 2015.
- J. Carreira and A. Zisserman, "Quo vadis, action recognition? a new model and the kinetics dataset," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, HI, USA, July 2017, pp. 4724-4733.
- S. Yeung et al., "Every moment counts: Dense detailed labeling of actions in complex videos," Int. J. Comput. Vis. vol. 126, 2018, pp. 375-389. https://doi.org/10.1007/s11263-017-1013-y
- Z. Shou et al., "Cdc: Convolutional-de-convolutional networks for precise temporal action localization in untrimmed videos," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, HI, USA, July 2017, pp. 1417-1426.