DOI: 10.57760/sciencedb.10304
title: CASIA-B Pose
title: CASIA-B Pose

description: Gait recognition as a prominent field within biometrics and computer vision focuses on analyzing the unique walking patterns of individuals for identification and security purposes. CASIA-B dataset encompasses a diverse collection of gait patterns with variations in walking styles, clothing, and viewpoints. This rich variety enables researchers to train and evaluate gait recognition algorithms on a comprehensive dataset that reflects real-world scenarios. Additionally, pose holds its own advantage in terms of robustness against carrying objects and clothing, making it particularly attractive for practical applications. However, most pose-based gait recognition methods tend to employ pose benchmarks calculated with different pose estimation algorithms, which led to duplicated work and biased comparisons. Hence, we introduce CASIA-B Pose, a pose-based gait recognition benchmark that utilizes 17 keypoints extracted by two state-of-the-art pose estimation algorithms, i.e. HRNet[1] and SimCC[2]. We also provide the mapping relations between RGB frames and keypoint tensors to foster further development. More details can be seen in [https://github.com/BNU-IVC/FastPoseGait].[1] Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, and Bin Xiao. "Deep High-Resolution Representation Learning for Visual Recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 10, pp. 3349-3364, 1 Oct. 2021, doi: 10.1109/TPAMI.2020.2983686.[2] Yanjie Li, Sen Yang, Peidong Liu, Shoukui Zhang, Yunxiao Wang, Zhicheng Wang, Wankou Yang, and Shu-Tao Xia. "SimCC: A Simple Coordinate Classification Perspective for Human Pose Estimation." Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VI. Cham: Springer Nature Switzerland, 2022.If you use our dataset in your research, please cite the corresponding paper:Yang Fu1, Shibei Meng1, Saihui Hou* and Xuecai Hu and Yongzhen Huang*. "GPGait: Generalized Pose-based Gait Recognition." arXiv preprint arXiv:2303.05234 (2023). (The first two authors contribute equally to this work)@article{fu2023gpgait,title={GPGait: Generalized Pose-based Gait Recognition},author={Fu, Yang and Meng, Shibei and Hou, Saihui and Hu, Xuecai and Huang, Yongzhen},journal={arXiv preprint arXiv:2303.05234},year={2023}}
description: Gait recognition as a prominent field within biometrics and computer vision focuses on analyzing the unique walking patterns of individuals for identification and security purposes. CASIA-B dataset encompasses a diverse collection of gait patterns with variations in walking styles, clothing, and viewpoints. This rich variety enables researchers to train and evaluate gait recognition algorithms on a comprehensive dataset that reflects real-world scenarios. Additionally, pose holds its own advantage in terms of robustness against carrying objects and clothing, making it particularly attractive for practical applications. However, most pose-based gait recognition methods tend to employ pose benchmarks calculated with different pose estimation algorithms, which led to duplicated work and biased comparisons. Hence, we introduce CASIA-B Pose, a pose-based gait recognition benchmark that utilizes 17 keypoints extracted by two state-of-the-art pose estimation algorithms, i.e. HRNet[1] and SimCC[2]. We also provide the mapping relations between RGB frames and keypoint tensors to foster further development. More details can be seen in [https://github.com/BNU-IVC/FastPoseGait].[1] Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, and Bin Xiao. "Deep High-Resolution Representation Learning for Visual Recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 10, pp. 3349-3364, 1 Oct. 2021, doi: 10.1109/TPAMI.2020.2983686.[2] Yanjie Li, Sen Yang, Peidong Liu, Shoukui Zhang, Yunxiao Wang, Zhicheng Wang, Wankou Yang, and Shu-Tao Xia. "SimCC: A Simple Coordinate Classification Perspective for Human Pose Estimation." Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VI. Cham: Springer Nature Switzerland, 2022.If you use our dataset in your research, please cite the corresponding paper:Yang Fu1, Shibei Meng1, Saihui Hou* and Xuecai Hu and Yongzhen Huang*. "GPGait: Generalized Pose-based Gait Recognition." arXiv preprint arXiv:2303.05234 (2023). (The first two authors contribute equally to this work)@article{fu2023gpgait,title={GPGait: Generalized Pose-based Gait Recognition},author={Fu, Yang and Meng, Shibei and Hou, Saihui and Hu, Xuecai and Huang, Yongzhen},journal={arXiv preprint arXiv:2303.05234},year={2023}}