Abstract
Fine-grained action localization in untrimmed sports videos presents a significant challenge due to rapid and subtle motion transitions over short durations. Existing supervised and weakly supervised solutions often rely on extensive annotated datasets and high-capacity models, making them computationally intensive and less adaptable to real-world scenarios. In this work, we introduce a lightweight and unsupervised skeleton-based action localization pipeline that leverages spatio-temporal graph neural representations. Our approach pre-trains an Attention-based Spatio-Temporal Graph Convolutional Network (ASTGCN) on a pose-sequence denoising task with blockwise partitions, enabling it to learn intrinsic motion dynamics without any manual labeling. At inference, we define a novel Action Dynamics Metric (ADM), computed directly from low-dimensional ASTGCN embeddings, which detects motion boundaries by identifying inflection points in its curvature profile. Our method achieves a mean Average Precision (mAP) of 82.66% and average localization latency of 29.09 ms on the DSV Diving dataset, matching state-of-the-art supervised performance while maintaining computational efficiency. Furthermore, it generalizes robustly to unseen, in-the-wild diving footage without retraining, demonstrating its practical applicability for lightweight, real-time action analysis systems in embedded or dynamic environments.
Abstract (translated)
未剪辑体育视频中的精细动作定位由于在短时间内快速而微妙的运动过渡，带来了重大挑战。现有的监督学习和弱监督解决方案通常依赖于大量注释数据集和高容量模型，这使得它们计算成本高昂且不太适应实际场景。在这项工作中，我们提出了一种基于骨架、轻量级且无监督的动作定位流水线，该管道利用了空间-时间图神经表示。我们的方法通过在分块划分的姿势序列去噪任务上对Attention-based Spatio-Temporal Graph Convolutional Network (ASTGCN)进行预训练，使其能够学习内在运动动力学而无需任何人工标注。 在推断阶段，我们定义了一种新颖的动作动态度量（ADM），该度量直接从低维ASTGCN嵌入中计算得出，并通过识别其曲率轮廓中的拐点来检测运动边界。我们的方法在DSV跳水数据集上实现了82.66%的平均精度(mAP)和29.09毫秒的平均定位延迟，同时匹配了最先进的监督性能并保持了计算效率。此外，该方法还能稳健地推广到未见过的真实场景中的潜水视频，无需重新训练，证明其在嵌入式或动态环境中实现轻量级、实时动作分析系统的实际应用性。
URL
https://arxiv.org/abs/2508.19647
