[1]LI Haomiao,ZHANG Hanxiao,XING Xianglei.Gait recognition with united local multiscale and global context features[J].CAAI Transactions on Intelligent Systems,2024,19(4):853-862.[doi:10.11992/tis.202304004]
Copy

Gait recognition with united local multiscale and global context features

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 4 Page number: 853-862 Column: 学术论文—机器学习 Public date: 2024-07-05
Title:
Gait recognition with united local multiscale and global context features
Author(s):
LI Haomiao;  ZHANG Hanxiao;  XING Xianglei
College of Intelligent Science and Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
biometric identification; gait recognition; cross-view; convolutional neural networks; deep learning; residual connection; fine-grained; attention mechanism
CLC:
TP391.41
DOI:
10.11992/tis.202304004
Abstract:
Existing gait recognition methods can extract rich gait information in the spatial dimension. However, they often overlook fine-grained temporal features within local regions and temporal contextual information across different sub-regions. Considering that gait recognition is a fine-grained recognition problem, and each individual’s gait carries unique temporal context information, we propose a gait recognition method that combines local multiscale and global contextual temporal features. The entire gait sequence is divided into multiple time resolutions and fine-grained temporal features within local sub-sequences are extracted. Transformer is used to extract temporal context information among different subsequences, and the global features are formed by integrating all subsequences based on the contextual information. We have conducted extensive experiments on two public datasets. The proposed model achieves rank-1 accuracies of 98.0%, 95.4%, and 87.0% on three walking conditions of the CASIA-B dataset. On the OU-MVLP dataset, the model achieves a rank-1 accuracy of 90.7%. The method proposed in this paper has achieved state-of-the-art results and can provide reference for other gait recognition methods.
References:
[1] 许文正, 黄天欢, 贲晛烨, 等. 跨视角步态识别综述[J]. 中国图象图形学报, 2023, 28(5): 1265–1286
XU Wenzheng, HUANG Tianhuan, BEN Xianye, et al. Cross-view gait recognition: a review[J]. Journal of image and graphics, 2023, 28(5): 1265–1286
[2] 吕卓纹, 王一斌, 邢向磊, 等. 加权CCA多信息融合的步态表征方法[J]. 智能系统学报, 2019, 14(3): 449–454
LYU Zhuowen, WANG Yibin, XING Xianglei, et al. A gait representation method based on weighted CCA for multi-information fusion[J]. CAAI transactions on intelligent systems, 2019, 14(3): 449–454
[3] 李一波, 李昆. 双视角下多特征信息融合的步态识别[J]. 智能系统学报, 2013, 8(1): 74–79
LI Yibo, LI Kun. Gait recognition based on dual view and multiple feature information fusion[J]. CAAI transactions on intelligent systems, 2013, 8(1): 74–79
[4] CONNOR P, ROSS A. Biometric recognition by gait: a survey of modalities and features[J]. Computer vision & image understanding, 2018, 167(1): 1–27.
[5] LIAO R, CAO C, GARCIA E B, et al. Pose-based temporal-spatial network (PTSN) for gait recognition with carrying and clothing variations[C]//12th Chinese Conference on Biometric Recognition. Shenzhen: Springer international publishing, 2017: 474-483.
[6] YU Shiqi, TAN Daoliang, TAN Tieniu. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition[C]//18th International Conference on Pattern Recognition. Hong Kong: IEEE, 2006, 4: 441-444.
[7] ZHANG Yuqi, HUANG Yongzhen, YU Shiqi, et al. Cross-view gait recognition by discriminative feature learning[J]. IEEE transactions on image processing, 2019, 29: 1001–1015.
[8] FAN Chao, PENG Yunjie, CAO Chunshui, et al. Gaitpart: Temporal part-based model for gait recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 14225-14233.
[9] SEPAS-MOGHADDAM A, ETEMAD A. View-invariant gait recognition with attentive recurrent learning of partial representations[J]. IEEE transactions on biometrics, behavior, and identity science, 2020, 3(1): 124–137.
[10] LIN Beibei, ZHANG Shunli, YU Xin. Gait recognition via effective global-local feature representation and local temporal aggregation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 14648-14656.
[11] WOLF T, BABAEE M, RIGOLL G. Multi-view gait recognition using 3D convolutional neural networks[C]//2016 IEEE International Conference on Image Processing. Phoenix: IEEE, 2016: 4165-4169.
[12] HUANG Zhen, XUE Dixiu, SHEN Xu, et al. 3D local convolutional neural networks for gait recognition[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 14920-14929.
[13] ZHANG Ziyuan, TRAN Luan, LIU Feng, et al. On learning disentangled representations for gait recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2020, 44(1): 345–360.
[14] ZHANG Ziyuan, TRAN Luan, YIN Xi, et al. Gait recognition via disentangled representation learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 4710-4719.
[15] LIN Beibei, ZHANG Shunli, BAO Feng. Gait recognition with multiple-temporal-scale 3d convolutional neural network[C]//Proceedings of the 28th ACM International Conference on Multimedia. New York: ACM, 2020: 3054-3062.
[16] HUANG Xiaohu, ZHU Duowang, WANG Hao, et al. Context-sensitive temporal feature learning for gait recognition[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 12909-12918.
[17] JI Shuiwang, XU Wei, YANG Ming, et al. 3D convolutional neural networks for human action recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2012, 35(1): 221–231.
[18] TRAN D, BOURDEV L, FERGUS R, et al. Learning spatiotemporal features with 3d convolutional networks[C]//Proceedings of the IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 4489-4497.
[19] TRAN D, WANG H, TORRESANI L, et al. A closer look at spatiotemporal convolutions for action recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 6450-6459.
[20] QIU Zhaofan, YAO Ting, MEI Tao. Learning spatio-temporal representation with pseudo-3D residual networks[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Venice: IEEE, 2017: 5534-5542.
[21] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30: 6000–6010.
[22] CAMGOZ N C, KOLLER O, HADFIELD S, et al. Sign language transformers: Joint end-to-end sign language recognition and translation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 10023-10033.
[23] VAROL G, MOMENI L, ALBANIE S, et al. Read and attend: temporal localisation in sign language videos[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 16857-16866.
[24] CAMGOZ N C, KOLLER O, HADFIELDS, et al. Multi-channel transformers for multi-articulatory sign language translation[C]//16th European Conference on Computer Vision. Glasgow: Springer International Publishing, 2020: 301-319.
[25] SAUNDERS B, CAMGOZ N C, BOWDEN R. Progressive transformers for end-to-end sign language production[C]//16th European Conference on Computer Vision. Glasgow: Springer International Publishing, 2020: 687-705.
[26] CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers[C]//16th European Conference on Computer Vision. Glasgow: Springer International Publishing, 2020: 213-229.
[27] FU Jun, LIU Jing, TIAN Haijie, et al. Dual attention network for scene segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 3146-3154.
[28] SUN Chen, MYERS A, VONDRICK C, et al. Videobert: a joint model for video and language representation learning[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 7464-7473.
[29] TAKEMURA N, MAKIHARA Y, MURAMATSU D, et al. Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition[J]. IPSJ transactions on computer vision and applications, 2018, 10: 1–14.
[30] KINGMA D P, BA J. Adam: a method for stochastic optimization[J]. International conference on learning representations, 2014, 1: 1–13.
[31] MAAS A L, HANNUN A Y, NG A Y. Rectifier nonlinearities improve neural network acoustic models[J]. Computer science, 2013, 30(1): 3–8.
[32] CHAO Hanqing, WANG Kun, HE Yiwei, et al. GaitSet: cross-view gait recognition through utilizing gait as a deep set[J]. IEEE transactions on pattern analysis and machine intelligence, 2021, 44(7): 3467–3478.
[33] 刘正道, 努尔毕亚·亚地卡尔, 木特力甫·马木提, 等. 基于优化GaitSet模型的步态识别研究[J]. 东北师范大学学报, 2022, 54(4): 77–86
LIU Zhengdao, NURBIYA·Yadikar, MUTELEP·Mamut, et al. Research on gait recognition based on optimized GaitSet model[J]. Journal of Northeastern University, 2022, 54(4): 77–86
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems