FSD-10: A fine-grained classification dataset for figure skating

doi:10.1016/j.neucom.2020.06.108

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 机器人应用与理论组

	FSD-10: A fine-grained classification dataset for figure skating
	Liu, Shenglan 3,4; Liu, Xiang 4; Huang, Gao 1; Qiao, Hong 2; Hu, Lianyu 4; Jiang, Dong 4; Zhang, Aibin 4; Liu, Yang 4; Guo, Ge 4
刊名	NEUROCOMPUTING
	2020-11-06
卷号	413 页码:360-367
关键词	Action recognition Figure Skating Dataset Fine-grained sports content analysis Keyframe based temporal segment network
ISSN号	0925-2312
DOI	10.1016/j.neucom.2020.06.108
通讯作者	Liu, Shenglan(liusl@mail.dlut.edu.cn)
英文摘要	Action recognition is an important and challenging problem in video analysis. Although the past decade has witnessed progress in action recognition with the development of deep learning, such process has been slow in competitive sports content analysis. To promote the research on action recognition from competitive sports video clips, we introduce a Figure Skating Dataset (FSD-10) for fine-grained sports content analysis. To this end, we collect 1484 clips from the worldwide figure skating championships in 2017-2018, which consist of 10 different actions in men/ladies programs. Each clip is at a rate of 30 frames per second with resolution 1080 x 720, which are annotated by experts. To build a baseline for action recognition in figure skating, we evaluate state-of-the-art action recognition methods on FSD-10. Motivated by the idea that domain knowledge is of great concern in sports field, we propose a key-frame based temporal segment network (KTSN) for classification and achieve remarkable performance. Experimental results demonstrate that FSD-10 is an ideal dataset for benchmarking action recognition algorithms, as it requires to accurately extract action motions rather than action poses. We hope FSD-10, which is designed to have a large collection of finegrained actions, can serve as a new challenge to develop more robust and advanced action recognition models. (C) 2020 Elsevier B.V. All rights reserved.
资助项目	National Key Research and Development Program of China[2017YFB1300200] ; National Key Research and Development Program of China[2017YFB1300203] ; Fundamental Research Funds for the Central Universities[DUT20RC(5)010]
WOS关键词	ACTION RECOGNITION
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000579803700030
资助机构	National Key Research and Development Program of China ; Fundamental Research Funds for the Central Universities
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42172]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
通讯作者	Liu, Shenglan
作者单位	1.Tsinghua Univ, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 3.Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian, Liaoning, Peoples R China 4.Dalian Univ Technol, Sch Innovat & Entrepreneurship, Dalian, Liaoning, Peoples R China
推荐引用方式 GB/T 7714	Liu, Shenglan,Liu, Xiang,Huang, Gao,et al. FSD-10: A fine-grained classification dataset for figure skating[J]. NEUROCOMPUTING,2020,413:360-367.
APA	Liu, Shenglan.,Liu, Xiang.,Huang, Gao.,Qiao, Hong.,Hu, Lianyu.,...&Guo, Ge.(2020).FSD-10: A fine-grained classification dataset for figure skating.NEUROCOMPUTING,413,360-367.
MLA	Liu, Shenglan,et al."FSD-10: A fine-grained classification dataset for figure skating".NEUROCOMPUTING 413(2020):360-367.