Abstract
Currently, two challenges exist in the field of gait recognition: (1) there is a lack of gait datasets that include common accompanying behaviors during walking, and (2) it’s necessary to improve feature representation in skeleton sequence data for model-based approaches. To address these concerns, we focused on the study of accompanying behavior-based walking conditions and multiple views, and utilizes depth cameras to collect gait data. We presented the CDUT Gait dataset to investigate the impact of various accompanying behaviors on gait recognition performance. And we proposed a RepGCN, a novel graph convolution networks model with innovative residual strategy in the spatial module, as well as new spatio-temporal feature extraction modules. Experiments demonstrate that RepGCN achieves state-of-the-art performance on CDUT Gait with minimal model parameters compared to existing model-based approaches. The combination of depth cameras and RepGCN has potential applications in access control, smart home, and anti-terrorism areas.
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References
Zou, Q., Ni, L., Wang, Q., et al.: Robust gait recognition by integrating inertial and RGBD sensors. IEEE Trans. Cybern. 48, 1136–1150 (2018). https://doi.org/10.1109/TCYB.2017.2682280
Yu, S., Wang, Q., Huang, Y.: A large RGB-D gait dataset and the baseline algorithm. In: Sun, Z., Shan, S., Yang, G., Zhou, J., Wang, Y., Yin, Y. (eds.) CCBR 2013. LNCS, vol. 8232, pp. 417–424. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-02961-0_52
Wang, Y., Sun, J., Li, J., Zhao, D.: Gait recognition based on 3D skeleton joints captured by kinect. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3151–3155. IEEE, Phoenix (2016)
Sivapalan, S., Chen, D., Denman, S., et al.: Gait energy volumes and frontal gait recognition using depth images. In: 2011 International Joint Conference on Biometrics (IJCB), pp 1–6. IEEE, Washington, DC (2011)
Hofmann, M., Geiger, J., Bachmann, S., et al.: The TUM gait from audio, image and depth (GAID) database: multimodal recognition of subjects and traits. J. Vis. Commun. Image Represent. 25, 195–206 (2014). https://doi.org/10.1016/j.jvcir.2013.02.006
Li, N., Zhao, X.: A multi-modal dataset for gait recognition under occlusion. Appl. Intell. 53, 1517–1534 (2023). https://doi.org/10.1007/s10489-022-03474-8
Marin-Jimenez, M.J., Castro, F.M., Delgado-Escano, R., et al.: UGaitNet: multimodal gait recognition with missing input modalities. IEEE Trans. Inform. Forensic Secur. 16, 5452–5462 (2021). https://doi.org/10.1109/TIFS.2021.3132579
Bari, A.S.M.H., Gavrilova, M.L.: Artificial neural network based gait recognition using kinect sensor. IEEE Access 7, 162708–162722 (2019). https://doi.org/10.1109/ACCESS.2019.2952065
Filipi Gonçalves dos Santos, C., Oliveira, D.D.S., Passos, L.A., et al.: Gait recognition based on deep learning: a survey. ACM Comput. Surv. 55, 1–34 (2023). https://doi.org/10.1145/3490235
Li, G., Muller, M., Thabet, A., Ghanem, B.: DeepGCNs: can GCNs go as deep as CNNs? In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9266–9275. IEEE, Seoul (2019)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations (ICLR 2017), Conference Track Proceedings, Toulon, 24–26 April 2017 (2017)
Teepe, T., Khan, A., Gilg, J., et al.: Gaitgraph: graph convolutional network for skeleton-based gait recognition. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 2314–2318. IEEE, Anchorage (2021)
Ding, X., Zhang, X., Ma, N., et al.: RepVGG: making VGG-style ConvNets great again. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13728–13737. IEEE, Nashville (2021)
Sepas-Moghaddam, A., Etemad, A.: Deep gait recognition: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 45, 264–284 (2023). https://doi.org/10.1109/TPAMI.2022.3151865
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 3844–3852. Curran Associates Inc., Red Hook (2016)
Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. AAAI 32 (2018). https://doi.org/10.1609/aaai.v32i1.12328
Liao, R., Yu, S., An, W., Huang, Y.: A model-based gait recognition method with body pose and human prior knowledge. Pattern Recogn. 98, 107069 (2020). https://doi.org/10.1016/j.patcog.2019.107069
Pinyoanuntapong, E., Ali, A., Wang, P., et al.: Gaitmixer: Skeleton-based gait representation learning via wide-spectrum multi-axial mixer. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023–2023), pp. 1–5. IEEE, Rhodes Island (2023)
Acknowledgements
This work was partially supported by Key R&D support projects 2021-YF05-02175-SN of the Chengdu Science and Technology Bureau, by the Key Project 2023YFG0271 of the Science and Technology Department of Sichuan Province, and by the funding of the China Scholarship Council.
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Mei, Z., Mei, Z., Tong, H., Yi, S., Zeng, H., Li, Y. (2024). RepGCN: A Novel Graph Convolution-Based Model for Gait Recognition with Accompanying Behaviors. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14429. Springer, Singapore. https://doi.org/10.1007/978-981-99-8469-5_12
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