论文标题
基于骨骼的关系推理进行小组活动分析
Skeleton-based Relational Reasoning for Group Activity Analysis
论文作者
论文摘要
对小组活动识别的研究主要依靠标准的两流方法（RGB和光流）作为其输入特征。很少有人探索了明确的姿势信息，没有直接使用它来推理人们的互动。在本文中，我们利用骨骼信息直接从中学习个人之间的相互作用。使用我们提出的方法GIRN，从独立的模块中推断出多种关系类型，这些模块描述了人体关节逐组的关系。除关节关系外，我们还尝试了以前未开发的个人与相关对象（例如排球）之间的关系。然后通过注意机制将不同的关系合并，这对那些对区分小组活动更重要的个人更为重要。我们在排球数据集中评估了我们的方法，并为最先进的方法获得了竞争结果。我们的实验证明了基于骨架的方法对建模多人相互作用的潜力。
Research on group activity recognition mostly leans on the standard two-stream approach (RGB and Optical Flow) as their input features. Few have explored explicit pose information, with none using it directly to reason about the persons interactions. In this paper, we leverage the skeleton information to learn the interactions between the individuals straight from it. With our proposed method GIRN, multiple relationship types are inferred from independent modules, that describe the relations between the body joints pair-by-pair. Additionally to the joints relations, we also experiment with the previously unexplored relationship between individuals and relevant objects (e.g. volleyball). The individuals distinct relations are then merged through an attention mechanism, that gives more importance to those individuals more relevant for distinguishing the group activity. We evaluate our method in the Volleyball dataset, obtaining competitive results to the state-of-the-art. Our experiments demonstrate the potential of skeleton-based approaches for modeling multi-person interactions.
