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Global Individual Interaction Network Based on Consistency for Group Activity Recognition

ELECTRONICS(2023)

Sun Yat Sen Univ | Brigham Young Univ

Cited 0|Views16
Abstract
Modeling the interactions among individuals in a group is essential for group activity recognition (GAR). Various graph neural networks (GNNs) are regarded as popular modeling methods for GAR, as they can characterize the interaction among individuals at a low computational cost. The performance of the current GNN-based modeling methods is affected by two factors. Firstly, their local receptive field in the mapping layer limits their ability to characterize the global interactions among individuals in spatial–temporal dimensions. Secondly, GNN-based GAR methods do not have an efficient mechanism to use global activity consistency and individual action consistency. In this paper, we argue that the global interactions among individuals, as well as the constraints of global activity and individual action consistencies, are critical to group activity recognition. We propose new convolutional operations to capture the interactions among individuals from a global perspective. We use contrastive learning to maximize the global activity consistency and individual action consistency for more efficient recognition. Comprehensive experiments show that our method achieved better GAR performance than the state-of-the-art methods on two popular GAR benchmark datasets.
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group activity recognition,deformable convolutional networks,contrastive learning
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要点】:本文针对群组活动识别问题,提出了一种基于一致性的全局个体交互网络模型,通过新的卷积操作和对比学习机制,有效提升了识别性能。

方法】:作者使用图神经网络(GNNs)并创新性地提出新的卷积操作,以全局视角捕捉个体间的交互,并通过对比学习增强全局活动和个体动作的一致性。

实验】:在两个流行的群组活动识别基准数据集上进行了全面实验,结果表明所提方法较现有先进方法取得了更好的识别性能。数据集名称未在摘要中提及。