构建首个女性运动动作小样本数据集，提升复杂动作识别准确率
Women Sport Actions Dataset for Visual Classification Using Small Scale Training Data
- 设计基于局部上下文通道注意力的CNN模型，增强特征表达
- 在自建数据集上达89.15% top-1准确率，跨数据集表现稳健
- 适合关注女性运动分析、小样本学习的研究者
基于图像的体育动作分类需处理复杂的身体姿态与人-物交互，是当前研究热点。尽管已有诸多基于机器学习的自动识别方法，但缺乏足够涵盖女性运动动作、具备类内与类间多样性的图像数据集。为此，本文提出名为WomenSports的新数据集，用于小样本条件下的女性运动分类。该数据集包含多种体育活动，覆盖广泛的动作、环境及球员间互动变化。同时，提出一种结合局部上下文区域通道注意力机制的卷积神经网络（CNN），以优化深度特征提取。在三个体育数据集和一个舞蹈数据集上进行实验，验证了算法的泛化能力。使用ResNet-50在WomenSports数据集上实现89.15%的top-1分类准确率，数据集已公开于Mendeley Data。
原文摘要 · Abstract (English)
Sports action classification representing complex body postures and player-object interactions is an emerging area in image-based sports analysis. Some works have contributed to automated sports action recognition using machine learning techniques over the past decades. However, sufficient image datasets representing women sports actions with enough intra- and inter-class variations are not available to the researchers. To overcome this limitation, this work presents a new dataset named WomenSports for women sports classification using small-scale training data. This dataset includes a variety of sports activities, covering wide variations in movements, environments, and interactions among players. In addition, this study proposes a convolutional neural network (CNN) for deep feature extraction. A channel attention scheme upon local contextual regions is applied to refine and enhance feature representation. The experiments are carried out on three different sports datasets and one dance dataset for generalizing the proposed algorithm, and the performances on these datasets are noteworthy. The deep learning method achieves 89.15% top-1 classification accuracy using ResNet-50 on the proposed WomenSports dataset, which is publicly available for research at Mendeley Data.
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