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DRGCN: Deep Relation GCN for Group Activity Recognition

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Neural Information Processing (ICONIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1332))

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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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References

  1. Amer, M.R., Lei, P., Todorovic, S.: HiRF: hierarchical random field for collective activity recognition in videos. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 572–585. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_37

    Chapter  Google Scholar 

  2. Azar, S.M., Atigh, M.G., Nickabadi, A., Alahi, A.: Convolutional relational machine for group activity recognition. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7884–7893 (2019)

    Google Scholar 

  3. Bagautdinov, T., Alahi, A., Fleuret, F., Fua, P., Savarese, S.: Social scene understanding: end-to-end multi-person action localization and collective activity recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4315–4324 (2017)

    Google Scholar 

  4. Choi, W., Shahid, K., Savarese, S.: What are they doing?: collective activity classification using spatio-temporal relationship among people. In: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp. 1282–1289. IEEE (2009)

    Google Scholar 

  5. Deng, Z., Vahdat, A., Hu, H., Mori, G.: Structure inference machines: recurrent neural networks for analyzing relations in group activity recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4772–4781 (2016)

    Google Scholar 

  6. Hu, G., Cui, B., He, Y., Yu, S.: Progressive relation learning for group activity recognition. ArXiv abs/1908.02948 (2019)

    Google Scholar 

  7. Ibrahim, M.S., Mori, G.: Hierarchical relational networks for group activity recognition and retrieval. In: Proceedings of the European Conference on Computer Vision, pp. 721–736 (2018)

    Google Scholar 

  8. Ibrahim, M.S., Muralidharan, S., Deng, Z., Vahdat, A., Mori, G.: A hierarchical deep temporal model for group activity recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1980 (2016)

    Google Scholar 

  9. Li, G., Muller, M., Thabet, A., Ghanem, B.: Deepgcns: can gcns go as deep as cnns? In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9267–9276 (2019)

    Google Scholar 

  10. Li, X., Choo Chuah, M.: Sbgar: Semantics based group activity recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2876–2885 (2017)

    Google Scholar 

  11. Qi, M., Qin, J., Li, A., Wang, Y., Luo, J., Van Gool, L.: stagnet: an attentive semantic rnn for group activity recognition. In: Proceedings of the European Conference on Computer Vision, pp. 101–117 (2018)

    Google Scholar 

  12. Shu, T., Todorovic, S., Zhu, S.C.: Cern: confidence-energy recurrent network for group activity recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5523–5531 (2017)

    Google Scholar 

  13. Wu, J., Wang, L., Wang, L., Guo, J., Wu, G.: Learning actor relation graphs for group activity recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9964–9974 (2019)

    Google Scholar 

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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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Correspondence to Yu Liu .

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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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  • DOI: https://doi.org/10.1007/978-3-030-63820-7_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63819-1

  • Online ISBN: 978-3-030-63820-7

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