In the dynamic and evolving field of computer vision, action recognition has
become a key focus, especially with the advent of sophisticated methodologies
like Convolutional Neural Networks (CNNs), Convolutional 3D, Transformer, and
spatial-temporal feature fusion. These technologies have shown promising
results on well-established benchmarks but face unique challenges in real-world
applications, particularly in sports analysis, where the precise decomposition
of activities and the distinction of subtly different actions are crucial.
Existing datasets like UCF101, HMDB51, and Kinetics have offered a diverse
range of video data for various scenarios. However, there's an increasing need
for fine-grained video datasets that capture detailed categorizations and
nuances within broader action categories. In this paper, we introduce the
VideoBadminton dataset derived from high-quality badminton footage. Through an
exhaustive evaluation of leading methodologies on this dataset, this study aims
to advance the field of action recognition, particularly in badminton sports.
The introduction of VideoBadminton could not only serve for badminton action
recognition but also provide a dataset for recognizing fine-grained actions.
The insights gained from these evaluations are expected to catalyze further
research in action comprehension, especially within sports contexts.

通过对高质量羽毛球镜头的全面评估，本研究旨在推进动作识别领域，尤其是在羽毛球运动中。引入 VideoBadminton 数据集不仅可以用于羽毛球动作识别，还可以提供一个识别细粒度动作的数据集。从这些评估中获得的见解有望催生更多在运动背景下的动作理解研究。