Abstract
This paper addresses the issue of insufficient attention to multi-scale spatiotemporal features in gait recognition algorithms for indoor scenes by designing a GaitTS (Gait Temporal-Spatial) algorithm that fuses multi-scale temporal and spatial dimension features. The proposed GaitTS algorithm processes temporal features by aggregating multiple frames of temporal information. Enhancing attention is paid to spatial features of different human body parts by partitioning feature maps into different scales. The dual focus equips the network with stronger capabilities for recognizing global and multi-scale local features, thereby improving the recognition performance of the model. Experimental results are implemented on CASIA-B, CCPG and occCASIA-B datasets. The results demonstrate that the proposed GaitTS algorithm achieves average Rank-1 accuracies of 97.9%, 95.4%, and 85.7% under normal (NM), walking with a bag (BG), and wearing a coat or jacket (CL) conditions for CASIA-B dataset, respectively, outperforming the state-of-the-art methods.
Similar content being viewed by others
Data availability
No datasets were generated or analysed during the current study.
References
Phillips, P.J.: Human identification technical challenges. In: Proceedings of International Conference on Image Processing, vol. 1, pp. 1–1. IEEE, Piscataway, NJ, USA (2002)
Wang, L., Tan, T., Hu, W., et al.: Automatic gait recognition based on statistical shape analysis. IEEE Trans. Image Process. 12(9), 1120–1131 (2003)
Yu, S., Tan, D., Tan, T.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: Proceedings of the IEEE 18th International Conference on Pattern Recognition, vol. 4, pp. 441–444. IEEE, Piscataway, NJ, USA (2006)
Chen, X., Liu, X., Liu, W., et al.: Explainable person re-identification with attribute-guided metric distillation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 11813–11822. IEEE, Piscataway, NJ, USA (2021)
Wang, K., Boonpratatong, A., Chen, W., Ren, L., Wei, G., Qian, Z., Lu, X., Zhao, D.: The fundamental property of human leg during walking: linearity and nonlinearity. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 4871–4881 (2023)
Wang, X., Zhang, R., Miao, Y., An, M., Wang, S., Zhang, Y.: \(\rm PI^{{\text{2 }}}\)-Based Adaptive Impedance Control for Gait Adaption of Lower Limb Exoskeleton. IEEE/ASME Trans. Mechatron. (2024). https://doi.org/10.1109/TMECH.2024.3370954
Sun, J., Zhou, L., Geng, B., Zhang, Y., Li, Y.: Leg State Estimation for Quadruped Robot by Using Probabilistic Model With Proprioceptive Feedback. IEEE/ASME Trans. Mechatron. (2024). https://doi.org/10.1109/TMECH.2024.3421251
Chao, H., He, Y., Zhang, J., et al.: Gaitset: Regarding gait as a set for cross-view gait recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8126–8133. AAAI, Menlo Park, California, USA (2019)
Fan, C., Peng, Y., Cao, C., et al.: Gaitpart: Temporal part-based model for gait recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14225–14233. IEEE, Piscataway, NJ, USA (2020)
Lin, B., Zhang, S., Yu, X.: Gait recognition via effective global-local feature representation and local temporal aggregation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14648–14656. IEEE, Piscataway, NJ, USA (2021)
Li, W., Hou, S., Zhang, C., et al.: An in-depth exploration of person re-identification and gait recognition in cloth-changing conditions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13824–13833 (2023)
Peng, Y., Cao, C., He, Z.: Occluded gait recognition. In: 2023 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2023). IEEE
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
Chu, L., Liu, Y., Wu, Z., et al.: Pp-humanseg: Connectivity-aware portrait segmentation with a large-scale teleconferencing video dataset. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 202–209. IEEE, Piscataway, NJ, USA (2022)
Yang, X., Zhou, Y., Zhang, T., et al.: Gait recognition based on dynamic region analysis. Signal Process. 88(9), 2350–2356 (2008)
Li, W., Kuo, C.C.J., Peng, J.: Gait recognition via gei subspace projections and collaborative representation classification. Neurocomputing 275, 1932–1945 (2018)
Alotaibi, M., Mahmood, A.: Improved gait recognition based on specialized deep convolutional neural network. Comput. Vis. Image Underst. 164, 103–110 (2017)
Lin, B., Zhang, S., Bao, F.: Gait recognition with multiple-temporal-scale 3d convolutional neural network. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 3054–3062. ACM, New York, USA (2020)
Dou, H., Zhang, P., Su, W., et al.: Metagait: Learning to learn an omni sample adaptive representation for gait recognition. In: Proceedings of the 2022 European Conference on Computer Vision, pp. 357–374. Springer, Berlin, Germany (2022)
Li, H., Qiu, Y., Zhao, H., et al.: Gaitslice: A gait recognition model based on spatio-temporal slice features. Pattern Recogn. 124, 108453 (2022)
Khan, M.H., Farid, M.S., Grzegorzek, M.: Spatiotemporal features of human motion for gait recognition. SIViP 13, 369–377 (2019)
Hou, S., Cao, C., Liu, X., et al.: Gait lateral network: Learning discriminative and compact representations for gait recognition. In: Proceedings of the 2020 European Conference on Computer Vision, pp. 382–398. Springer, Berlin, Germany (2020)
Huang, X., Zhu, D., Wang, H., et al.: Context-sensitive temporal feature learning for gait recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 12909–12918. IEEE, Piscataway, NJ, USA (2021)
Zhang, S., Wang, Y., Li, A.: Cross-view gait recognition with deep universal linear embeddings. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9095–9104. IEEE, Piscataway, NJ, USA (2021)
Chai, T., Li, A., Zhang, S., et al.: Lagrange motion analysis and view embeddings for improved gait recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20249–20258. IEEE, Piscataway, NJ, USA (2022)
Xue, W., Ai, H., Sun, T., et al.: Frame-gan: increasing the frame rate of gait videos with generative adversarial networks. Neurocomputing 380, 95–104 (2020)
Gupta, S.K.: Reduction of covariate factors from silhouette image for robust gait recognition. Multimed. Tools Appl. 80(28), 36033–36058 (2021)
Chen, X., Luo, X., Weng, J., et al.: Multi-view gait image generation for cross-view gait recognition. IEEE Trans. Image Process. 30, 3041–3055 (2021)
Cao, Z., Simon, T., Wei, S.E., et al.: Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291–7299. IEEE, Piscataway, NJ, USA (2017)
Lee, L., Grimson, W.E.L.: Gait analysis for recognition and classification. In: Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, pp. 155–162. IEEE, Piscataway, NJ, USA (2002)
Liu, W., Bao, Q., Sun, Y., et al.: Recent advances of monocular 2d and 3d human pose estimation: A deep learning perspective. ACM Comput. Surv. 55(4), 1–41 (2022)
Teepe, T., Khan, A., Gilg, J., et al.: Gaitgraph: Graph convolutional network for skeleton-based gait recognition. In: Proceedings of the 2021 IEEE International Conference on Image Processing, pp. 2314–2318. IEEE, Piscataway, NJ, USA (2021)
Liao, R., Yu, S., An, W., et al.: A model-based gait recognition method with body pose and human prior knowledge. Pattern Recogn. 98, 107069 (2020)
Loper, M., Mahmood, N., Romero, J., et al.: Smpl: a skinned multi-person linear model. ACM Trans. Gr. 34(6), 1–16 (2015)
Zheng, J., Liu, X., Liu, W., et al.: Gait recognition in the wild with dense 3d representations and a benchmark. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20228–20237. IEEE, Piscataway, NJ, USA (2022)
Fan, C., Liang, J., Shen, C., et al.: Opengait: Revisiting gait recognition towards better practicality. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9707–9716. IEEE, Piscataway, NJ, USA (2023)
Author information
Authors and Affiliations
Contributions
Langwen Zhang: Conceptualization; methodology; investigation; writing-original draft; writing-review and editing. Zihan Men: Writing-methodology; investigation; validation; writing-original draft. Wei Xie: Conceptualization; investigation; writing-review and editing.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no Conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhang, L., Men, Z. & Xie, W. Gaitts: indoor gait recognition with multi-scale temporal-spatial information aggregation. SIViP 19, 28 (2025). https://doi.org/10.1007/s11760-024-03611-5
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11760-024-03611-5