基于新型细粒度数据集的羽毛球动作识别基准测试
Benchmarking Badminton Action Recognition with a New Fine-Grained Dataset
AI总结：
本研究构建了基于高质量羽毛球素材的细粒度VideoBadminton数据集，通过在该数据集对主流动作识别方法进行全面评估，为羽毛球动作识别及细粒度动作识别研究提供新基准，推动体育场景下的动作理解研究。
摘要：
在动态发展的计算机视觉领域，动作识别已成为核心研究方向，尤其是随着卷积神经网络（CNNs）、3D卷积、Transformer以及时空特征融合等先进方法的出现，更是推动了该领域的发展。这些技术在成熟基准数据集上取得了不错的效果，但在实际应用中面临独特挑战，尤其是在体育分析场景中，活动的精准分解和细微动作的区分至关重要。现有数据集如UCF101、HMDB51和Kinetics已为各类场景提供了丰富的视频数据，但目前对能够捕捉宽泛动作类别下详细分类和细微差别的细粒度视频数据集需求日益增长。本文提出了基于高质量羽毛球赛事素材构建的VideoBadminton数据集。通过在该数据集上对主流方法进行全面评估，本研究旨在推动动作识别领域尤其是羽毛球运动方向的发展。VideoBadminton数据集不仅可用于羽毛球动作识别，还能为细粒度动作识别提供数据支撑。这些评估所得的见解有望推动动作理解领域的进一步研究，特别是在体育场景下的研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
