Abstract
In order to make better use of the temporal information of gait data and improve the accuracy of gait recognition. In this paper, we propose a multi-grained feature aggregate network(GaitMG), which contains two important modules: Multi-Grained Feature Aggregator(MGFA) and Spatio-Temporal Feature Fusion Module(STFFM). The MGFA: a novel applying of convolution, can tackle the problem of poor representation ability of single grained temporal features. STFFM can fuse the multi-grained temporal features obtained by MGFA, get a discriminative representation. On CASIA-B, our method can achieve rank-1 accuracy of 98.0% under normal walking condition, 94.1% under bag-carrying condition, 85.3% under coat-wearing condition.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (62072122), Key Construction Discipline Scientific Research Capacity Improvement Project of Guangdong Province (No.2021ZDJS025), Postgraduate Education Innovation Plan Project of Guangdong Province (2020SFKC054). The Special Projects in Key Fields of Ordinary Universities of Guangdong Province (2021ZDZX1087) and Guangzhou Science and Technology Plan Project (2023B03 J1327).
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The data that supports the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. The authors declare that they have no conflict of interest. This article does not contain any studies with human participants performed by any of the authors.
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Wan, J., Zhao, H., Li, R., Chen, R., Wei, T., Ren, Y. (2024). GaitMG: A Multi-grained Feature Aggregate Network for Gait Recognition. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2023. Lecture Notes in Computer Science(), vol 14374. Springer, Singapore. https://doi.org/10.1007/978-981-97-1417-9_13
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DOI: https://doi.org/10.1007/978-981-97-1417-9_13
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