Abstract
Person to person relation is an essential clue for group activity recognition (GAR). And the relation graph and the graph convolution neural network (GCN) have become powerful presentation and processing tools of relationship. The previous methods are difficult to capture the complex relationship between people. We propose an end-to-end framework called Deep Relation GCN (DRGCN) for recognizing group activities by exploring the high-level relations between individuals. In DRGCN, we use a horizontal slicing strategy to layer each individual into smaller individual parts, then apply a deep GCN to learn the relation graph of these individual parts. We perform experiments on two widely used datasets and obtain competitive results that demonstrated the effectiveness of our method.
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Acknowledgments
This work is supported by the National Natural Science Foundation in China (Grant: 61672128) and the Fundamental Research Fund for Central University (Grant: DUT20TD107).
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Feng, Y., Shan, S., Liu, Y., Zhao, Z., Xu, K. (2020). DRGCN: Deep Relation GCN for Group Activity Recognition. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_41
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