判别式
计算机科学
动作识别
水准点(测量)
人工智能
光学(聚焦)
动作(物理)
篮球
机器学习
模式识别(心理学)
深度学习
历史
量子力学
光学
物理
考古
地理
班级(哲学)
大地测量学
作者
Xiaofan Gu,Xinwei Xue,Feng Wang
标识
DOI:10.1109/icassp40776.2020.9053928
摘要
Currently most works on action recognition focus on the coarsely-grained actions, while the fine-grained action recognition is seldom addressed which is of vital importance in many applications such as video retrieval. To tackle this issue, in this paper, we release a challenging dataset by annotating the fine-grained actions in basketball game videos. A benchmark evaluation of the state-of-the-art approaches for action recognition is also provided on our dataset. Furthermore, we propose an approach by integrating the NTS-Net into two-stream network so as to locate the most informative regions and extract more discriminative features for fine-grained action recognition. Our experiments show that the proposed approach significantly outperforms the existing approaches.
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